MINES ParisTech

paca:ATLAS_PACA_ASPECT_SRTM_EPSG_4326_r32 paca:ATLAS_PACA_ASPECT_SRTM_EPSG_4326_r32 paca:ATLAS_PACA_BTI_05
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Interface
Web Service, OGC Web Map Service 1.3.0
Keywords
WFS, WMS, GEOSERVER
Fees
NONE
Access constraints
NONE
Supported languages
No INSPIRE Extended Capabilities (including service language support) given. See INSPIRE Technical Guidance - View Services for more information.
Data provider

MINES ParisTech (unverified)

Contact information:

Lionel MENARD

MINES ParisTech

Work:
1, rue Claude Daunesse - CS 10207, 06904 SOPHIA ANTIPOLIS, FRANCE

Email: 

Service metadata
No INSPIRE Extended Capabilities (including service metadata) given. See INSPIRE Technical Guidance - View Services for more information.

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Available map layers (783)

ATLAS_PACA_ASPECT_SRTM_EPSG_4326_r32 (paca:ATLAS_PACA_ASPECT_SRTM_EPSG_4326_r32)

Local azimuth (degree) of the Digital Elevation Model extracted from SRTMv4 (http://www.cgiar-csi.org/category/elevation/)

ATLAS_PACA_BTI_01 (paca:ATLAS_PACA_BTI_01)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 0.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_02 (paca:ATLAS_PACA_BTI_02)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_03 (paca:ATLAS_PACA_BTI_03)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_04 (paca:ATLAS_PACA_BTI_04)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 90.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_05 (paca:ATLAS_PACA_BTI_05)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 225.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_06 (paca:ATLAS_PACA_BTI_06)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 270.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_07 (paca:ATLAS_PACA_BTI_07)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 0.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_08 (paca:ATLAS_PACA_BTI_08)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 35.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_BTI_12 (paca:ATLAS_PACA_BTI_12)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Irradiation on Tilted plan (slope = 45.0 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DEM_SRTM_EPSG_4326_r32 (paca:ATLAS_PACA_DEM_SRTM_EPSG_4326_r32)

Digital Elevation Model extracted from SRTMv4 (http://www.cgiar-csi.org/data/elevation/item/45-srtm-90m-digital-elevation-database-v41)

ATLAS_PACA_DISTRICT_DIVISION_EPSG_4326_16b (paca:ATLAS_PACA_DISTRICT_DIVISION_EPSG_4326_16b)

Map of the district divisions for the solar atlas of Provence-Alpes-Cote d''Azur (PACA) It comprises the 964 districts of PACA with a surrounding margin of 4 km. It has been created from the product GEOFLA of IGN, the french institute of geography (http://professionnels.ign.fr/ficheProduitCMS.do?idDoc=5323861)

ATLAS_PACA_DISTRICT_DIVISION_EPSG_4326_SHP (paca:ATLAS_PACA_DISTRICT_DIVISION_EPSG_4326_SHP)

Shapefile of the districts in the region Provence-Alpes-Cote d'Azur (PACA) Extracted from the GEOFLA products of IGN, the Frence institute of geography

ATLAS_PACA_DNI (paca:ATLAS_PACA_DNI)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Beam Normal Irradiation in kWh/m^2 (2004-2010)

ATLAS_PACA_DNI_EPSG_4326_8b (paca:ATLAS_PACA_DNI_EPSG_4326_8b)

ATLAS_PACA_DTI_01 (paca:ATLAS_PACA_DTI_01)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 0.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_02 (paca:ATLAS_PACA_DTI_02)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_03 (paca:ATLAS_PACA_DTI_03)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_04 (paca:ATLAS_PACA_DTI_04)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 90.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_05 (paca:ATLAS_PACA_DTI_05)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 225.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_06 (paca:ATLAS_PACA_DTI_06)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 270.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_07 (paca:ATLAS_PACA_DTI_07)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 0.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_08 (paca:ATLAS_PACA_DTI_08)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 35.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_DTI_12 (paca:ATLAS_PACA_DTI_12)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Diffuse Irradiation on Tilted plan (slope = 45.0 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_01 (paca:ATLAS_PACA_GTI_01)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 0.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_02 (paca:ATLAS_PACA_GTI_02)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_03 (paca:ATLAS_PACA_GTI_03)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_04 (paca:ATLAS_PACA_GTI_04)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 90.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_05 (paca:ATLAS_PACA_GTI_05)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 225.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_06 (paca:ATLAS_PACA_GTI_06)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 270.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_07 (paca:ATLAS_PACA_GTI_07)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 16.7 deg, azimuth = 0.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_08 (paca:ATLAS_PACA_GTI_08)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 35.0 deg, azimuth = 180.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_GTI_12 (paca:ATLAS_PACA_GTI_12)

Solar Atlas of Provence-Alpes-Cte d'Azur: mean of yearly sum of Global Irradiation on Tilted plan (slope = 45.0 deg, azimuth = 135.0 deg) in kWh/m^2 (2004-2010)

ATLAS_PACA_LAND_USE_CLC2006_EPSG_4326_8b (paca:ATLAS_PACA_LAND_USE_CLC2006_EPSG_4326_8b)

Raster data on land cover from the CLC2006 inventory, extracted for Provence-Alpes-Cote d'Azur. Data categorized using the 44 classes of the 3-level Corine nomenclature Extracted with the wms request: http://sd1878-2.sivit.org/geoserver/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&BBOX=3.91625,42.916667,8.01625,45.416667&LAYERS=topp:RCLC06_WGS&CRS=EPSG:4326&FORMAT=image/geotiff 8&WIDTH=1640&HEIGHT=1500

ATLAS_PACA_MASK_EPSG_4326_8b (paca:ATLAS_PACA_MASK_EPSG_4326_8b)

Binary mask for the solar atlas of Provence-Alpes-Cote d'Azur (PACA) It comprises the five territorial regions of PACA with a surrounding margin of 4 km

ATLAS_PACA_MAX_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32)

Description: Map of estimated monthly maximum daytime air temperature at 2-m height [°C], March 2009, PACA Region. The layer was generated from input satellite MODIS images (provided by NASA) and in-situ temperature measurements (collected by Meteo France and provided by ARMINES) through the application of a supervised regression techique based on support vector machines (SVM). Daytime MODIS acquisitions are daily. For each pixel, first air temperature was separately estimated from each daily MODIS observation not affected by cloud cover, and, then, the maximum of the resulting estimates was displayed in the present layer. Pixels in which cloud cover was present at all observation times and sea areas were masked out. Scientific reference: G. Moser and S. B. Serpico,

ATLAS_PACA_MAX_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32)

Max temperature of the month 01 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32)

Max temperature of the month 02 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32)

Max temperature of the month 03 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32)

Max temperature of the month 04 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32)

Max temperature of the month 05 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32)

Max temperature of the month 06 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32)

Max temperature of the month 07 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32)

Max temperature of the month 08 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32)

Max temperature of the month 09 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32)

Max temperature of the month 10 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32)

Max temperature of the month 11 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MAX_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MAX_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32)

Max temperature of the month 12 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32)

Map of estimated monthly mean daytime air temperature at 2-m height [°C], March 2009, PACA Region. The layer was generated from input satellite MODIS images (provided by NASA) and in-situ temperature measurements (collected by Meteo France and provided by ARMINES) through the application of a supervised regression techique based on support vector machines (SVM). Daytime MODIS acquisitions are daily. For each pixel, first air temperature was separately estimated from each daily MODIS observation not affected by cloud cover, and, then, the mean of the resulting estimates was displayed in the present layer. Pixels in which cloud cover was present at all observation times and sea areas were masked out. Scientific reference: G. Moser and S. B. Serpico,

ATLAS_PACA_MEAN_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32)

Mean temperature of the month 01 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32)

Mean temperature of the month 02 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32)

Mean temperature of the month 03 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32)

Mean temperature of the month 04 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32)

Mean temperature of the month 05 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32)

Mean temperature of the month 06 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32)

Mean temperature of the month 07 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32)

Mean temperature of the month 08 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32)

Mean temperature of the month 09 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32)

Mean temperature of the month 10 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32)

Mean temperature of the month 11 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32)

Mean temperature of the month 12 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MEAN_TEMPERATURE_MSG_UNIGE_EPSG_4326_r32 (paca:ATLAS_PACA_MEAN_TEMPERATURE_MSG_UNIGE_EPSG_4326_r32)

Description: Map of estimated mean daytime air temperature at 2-m height [Degree C], August 30, 2009, PACA Region. The layer was generated from input satellite MSG-SEVIRI images (provided by EUMETSAT) and in-situ temperature measurements (collected by Meteo France and provided by ARMINES) through the application of a supervised regression techique based on support vector machines (SVM). Hourly MSG-SEVIRI images acquired between 8 am and 5 pm were used. For each pixel, first air temperature was separately estimated from each hourly MSG-SEVIRI observation not affected by cloud cover, and, then, the mean of the resulting estimates was displayed in the present layer. Pixels in which cloud cover was present at all observation times and sea areas were masked out. Scientific reference: G. Moser and S. B. Serpico.

ATLAS_PACA_MIN_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32)

Description: Map of estimated monthly minimum daytime air temperature at 2-m height [°C], March 2009, PACA Region. The layer was generated from input satellite MODIS images (provided by NASA) and in-situ temperature measurements (collected by Meteo France and provided by ARMINES) through the application of a supervised regression techique based on support vector machines (SVM). Daytime MODIS acquisitions are daily. For each pixel, first air temperature was separately estimated from each daily MODIS observation not affected by cloud cover, and, then, the minimum of the resulting estimates was displayed in the present layer. Pixels in which cloud cover was present at all observation times and sea areas were masked out. Scientific reference: G. Moser and S. B. Serpico,

ATLAS_PACA_MIN_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH01_NCEP_EPSG_4326_r32)

Min temperature of the month 01 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH02_NCEP_EPSG_4326_r32)

Min temperature of the month 02 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH03_NCEP_EPSG_4326_r32)

Min temperature of the month 03 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH04_NCEP_EPSG_4326_r32)

Min temperature of the month 04 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH05_NCEP_EPSG_4326_r32)

Min temperature of the month 05 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH06_NCEP_EPSG_4326_r32)

Min temperature of the month 06 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH07_NCEP_EPSG_4326_r32)

Min temperature of the month 07 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH08_NCEP_EPSG_4326_r32)

Min temperature of the month 08 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH09_NCEP_EPSG_4326_r32)

Min temperature of the month 09 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH10_NCEP_EPSG_4326_r32)

Min temperature of the month 10 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH11_NCEP_EPSG_4326_r32)

Min temperature of the month 11 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_MIN_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32 (paca:ATLAS_PACA_MIN_TEMPERATURE_MONTH12_NCEP_EPSG_4326_r32)

Min temperature of the month 12 extracted from the webservice EMPClimate (http://www.webservice-energy.org/web-services/w3c-web-services/emp-climate-w3c)

ATLAS_PACA_SLOPE_SRTM_EPSG_4326_r32 (paca:ATLAS_PACA_SLOPE_SRTM_EPSG_4326_r32)

Local slope in % (atan of the slope angle) of the Digital Elevation Model extracted from SRTMv4 (http://www.cgiar-csi.org/data/elevation/item/45-srtm-90m-digital-elevation-database-v41)

ATLAS_PACA_SOURCE_POINTS_DISTKM_EPSG_4326_r32 (paca:ATLAS_PACA_SOURCE_POINTS_DISTKM_EPSG_4326_r32)

Distance to the nearest electric source point (km) (from scanned map of ErDF)

ATLAS_PACA_SOURCE_POINTS_DISTKM_EPSG_4326_shp (paca:ATLAS_PACA_SOURCE_POINTS_DISTKM_EPSG_4326_shp)

ATLAS_PACA_STD_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32 (paca:ATLAS_PACA_STD_TEMPERATURE_MODIS_UNIGE_EPSG_4326_r32)

Description: Map of the standard deviation of the regression error in the estimated monthly mean daytime air temperature at 2-m height [°C], March 2009, PACA Region. The layer was generated from input satellite MODIS images (provided by NASA) and in-situ temperature measurements (collected by Meteo France and provided by ARMINES) through the application of a nonstationary probabilistic model based on support vector machines (SVM). Daytime MODIS acquisitions are daily. For each pixel, first the regression error statistics was separately modeled from each daily MODIS observation not affected by cloud cover, and, then, the standard deviation of the mean temperature estimate was derived and displayed in the present layer. Pixels in which cloud cover was present at all observation times and sea areas were masked out. Scientific reference: G. Moser and S. B. Serpico,

ATLAS_PACA_TERRITORIAL_DIVISION_EPSG_4326_8b (paca:ATLAS_PACA_TERRITORIAL_DIVISION_EPSG_4326_8b)

Map of the territorial divisions for the solar atlas of Provence-Alpes-Cote d'Azur (PACA) It comprises the six territorial regions of PACA with a surrounding margin of 4 km It has been created from the product GEOFLA of IGN, the French institute of geography (http://professionnels.ign.fr/ficheProduitCMS.do?idDoc=5323861)

ATLAS_PACA_TERRITORIAL_DIVISION_EPSG_4326_SHP (paca:ATLAS_PACA_TERRITORIAL_DIVISION_EPSG_4326_SHP)

Shapefile of the territorial divisions for the solar atlas of Provence-Alpes-Cote d'Azur (PACA). It comprises the six territorial regions of PACA with a surrounding margin of 4 km

ATLAS_PACA_WINDSPEED_10M_CLASSES_EPSG_4326_8b (paca:ATLAS_PACA_WINDSPEED_10M_CLASSES_EPSG_4326_8b)

ATLAS_PACA_ZONE_FLOOD_RISK_EPSG_4326_8b (paca:ATLAS_PACA_ZONE_FLOOD_RISK_EPSG_4326_8b)

Map of flood risks (Atlas Zone Inondable validé) extracted from the GIS CARMEN (DREAL - PACA). (http://carmen.developpement-durable.gouv.fr/25/environnement.map)

ATLAS_PACA_ZONE_NATURE_RESERVES_EPSG_4326_8b (paca:ATLAS_PACA_ZONE_NATURE_RESERVES_EPSG_4326_8b)

Map of natural reserves (Parc National" + "Réserve naturelle nationale" + "Réserve naturelle régionale" + "Parc naturel régional") extracted from the GIS CARMEN (DREAL - PACA). (http://carmen.developpement-durable.gouv.fr/25/environnement.map)

Average value of current power (marine:Average_value_of_current_power)

Map of averaged values of depth-averaged current power. Information was produced using a 5-year time series extracted from Homere sea state hindcast (Boudière et al. 2013, Boudière et al. 2014). References: E. Boudière, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, “A suitable metocean hindcast database for the design of Marine energy converters,” International Journal of Marine Energy, vol. 3–4, pp. e40–e52, Dec. 2013. E. Boudière and C. Maisondieu, “Manuel de l’utilisateur de la base de données HOMERE,” 2014.

Average value of significant wave height (marine:Average_value_of_significant_wave_height)

Average value of Hs Abstract : Map of averaged values of significant wave height (Hs). Information was produced using a 5-year time series extracted from Homere sea state hindcast (Boudière et al. 2013, Boudière et al. 2014). References: E. Boudière, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, “A suitable metocean hindcast database for the design of Marine energy converters,” International Journal of Marine Energy, vol. 3–4, pp. e40–e52, Dec. 2013. E. Boudière and C. Maisondieu, “Manuel de l’utilisateur de la base de données HOMERE,” 2014.

Average value of wave energy period (Te) (marine:Average_value_of_wave_energy_period_Te)

Map of averaged values of wave energy period. Information was produced using a 5-year time series extracted from Homere sea state hindcast (BoudiËre et al. 2013, BoudiËre et al. 2014). References: E. BoudiËre, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, ìA suitable metocean hindcast database for the design of Marine energy converters,î International Journal of Marine Energy, vol. 3ñ4, pp. e40ñe52, Dec. 2013. E. BoudiËre and C. Maisondieu, ìManuel de líutilisateur de la base de donnÈes HOMERE,î 2014.

Average value of wave power density (Te) (marine:Average_value_of_wave_power_density_Te)

Map of averaged values of wave power density. Information was produced using a 5-year time series extracted from Homere sea state hindcast (Boudière et al. 2013, Boudière et al. 2014). References: E. Boudière, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, “A suitable metocean hindcast database for the design of Marine energy converters,” International Journal of Marine Energy, vol. 3–4, pp. e40–e52, Dec. 2013. E. Boudière and C. Maisondieu, “Manuel de l’utilisateur de la base de données HOMERE,” 2014.

Europe_life_expectancies (energeo_pia:Europe_life_expectancies)

Life Expectancy for population over 30 years in 2005

GAINS_BIO_CLE_O3_2005_LLE1 (energeo_pia_2013:GAINS_BIO_CLE_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2005_LLE2 (energeo_pia_2013:GAINS_BIO_CLE_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2030_LLE1 (energeo_pia_2013:GAINS_BIO_CLE_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2030_LLE2 (energeo_pia_2013:GAINS_BIO_CLE_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2040_LLE1 (energeo_pia_2013:GAINS_BIO_CLE_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2040_LLE2 (energeo_pia_2013:GAINS_BIO_CLE_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2050_LLE1 (energeo_pia_2013:GAINS_BIO_CLE_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_O3_2050_LLE2 (energeo_pia_2013:GAINS_BIO_CLE_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Open Europe scenario (CLE)

GAINS_BIO_CLE_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_BIO_CLE_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (CLE)

GAINS_BIO_CLE_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_BIO_CLE_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (CLE)

GAINS_BIO_CLE_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_BIO_CLE_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (CLE)

GAINS_BIO_CLE_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_BIO_CLE_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (CLE)

GAINS_BIO_MFR_O3_2005_LLE1 (energeo_pia_2013:GAINS_BIO_MFR_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2005_LLE2 (energeo_pia_2013:GAINS_BIO_MFR_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2030_LLE1 (energeo_pia_2013:GAINS_BIO_MFR_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2030_LLE2 (energeo_pia_2013:GAINS_BIO_MFR_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2040_LLE1 (energeo_pia_2013:GAINS_BIO_MFR_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2040_LLE2 (energeo_pia_2013:GAINS_BIO_MFR_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2050_LLE1 (energeo_pia_2013:GAINS_BIO_MFR_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_O3_2050_LLE2 (energeo_pia_2013:GAINS_BIO_MFR_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Open Europe scenario (MTFR)

GAINS_BIO_MFR_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_BIO_MFR_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (MTFR)

GAINS_BIO_MFR_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_BIO_MFR_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (MTFR)

GAINS_BIO_MFR_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_BIO_MFR_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (MTFR)

GAINS_BIO_MFR_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_BIO_MFR_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Open Europe scenario (MTFR)

GAINS_BL_CLE_O3_2005_LLE1 (energeo_pia_2013:GAINS_BL_CLE_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2005_LLE2 (energeo_pia_2013:GAINS_BL_CLE_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2020_LLE1 (energeo_pia_2013:GAINS_BL_CLE_O3_2020_LLE1)

Premature mortality in 2020 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2020_LLE2 (energeo_pia_2013:GAINS_BL_CLE_O3_2020_LLE2)

Premature mortality in 2020 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2030_LLE1 (energeo_pia_2013:GAINS_BL_CLE_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2030_LLE2 (energeo_pia_2013:GAINS_BL_CLE_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2040_LLE1 (energeo_pia_2013:GAINS_BL_CLE_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2040_LLE2 (energeo_pia_2013:GAINS_BL_CLE_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2050_LLE1 (energeo_pia_2013:GAINS_BL_CLE_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_O3_2050_LLE2 (energeo_pia_2013:GAINS_BL_CLE_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Baseline scenario (CLE)

GAINS_BL_CLE_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_BL_CLE_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (CLE)

GAINS_BL_CLE_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_BL_CLE_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (CLE)

GAINS_BL_CLE_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_BL_CLE_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (CLE)

GAINS_BL_CLE_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_BL_CLE_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (CLE)

GAINS_BL_FEF_O3_2005_LLE1 (energeo_pia_2013:GAINS_BL_FEF_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2005_LLE2 (energeo_pia_2013:GAINS_BL_FEF_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2020_LLE1 (energeo_pia_2013:GAINS_BL_FEF_O3_2020_LLE1)

Premature mortality in 2020 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2020_LLE2 (energeo_pia_2013:GAINS_BL_FEF_O3_2020_LLE2)

Premature mortality in 2020 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2030_LLE1 (energeo_pia_2013:GAINS_BL_FEF_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2030_LLE2 (energeo_pia_2013:GAINS_BL_FEF_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2040_LLE1 (energeo_pia_2013:GAINS_BL_FEF_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2040_LLE2 (energeo_pia_2013:GAINS_BL_FEF_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2050_LLE1 (energeo_pia_2013:GAINS_BL_FEF_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_O3_2050_LLE2 (energeo_pia_2013:GAINS_BL_FEF_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Baseline scenario (FEF)

GAINS_BL_FEF_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_BL_FEF_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (FEF)

GAINS_BL_FEF_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_BL_FEF_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (FEF)

GAINS_BL_FEF_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_BL_FEF_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (FEF)

GAINS_BL_FEF_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_BL_FEF_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (FEF)

GAINS_BL_MFR_O3_2005_LLE1 (energeo_pia_2013:GAINS_BL_MFR_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2005_LLE2 (energeo_pia_2013:GAINS_BL_MFR_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2020_LLE1 (energeo_pia_2013:GAINS_BL_MFR_O3_2020_LLE1)

Premature mortality in 2020 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2020_LLE2 (energeo_pia_2013:GAINS_BL_MFR_O3_2020_LLE2)

Premature mortality in 2020 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2030_LLE1 (energeo_pia_2013:GAINS_BL_MFR_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2030_LLE2 (energeo_pia_2013:GAINS_BL_MFR_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2040_LLE1 (energeo_pia_2013:GAINS_BL_MFR_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2040_LLE2 (energeo_pia_2013:GAINS_BL_MFR_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2050_LLE1 (energeo_pia_2013:GAINS_BL_MFR_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_O3_2050_LLE2 (energeo_pia_2013:GAINS_BL_MFR_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Baseline scenario (MTFR)

GAINS_BL_MFR_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_BL_MFR_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (MTFR)

GAINS_BL_MFR_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_BL_MFR_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (MTFR)

GAINS_BL_MFR_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_BL_MFR_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (MTFR)

GAINS_BL_MFR_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_BL_MFR_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Baseline scenario (MTFR)

GAINS_MAX_CLE_O3_2005_LLE1 (energeo_pia_2013:GAINS_MAX_CLE_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2005_LLE2 (energeo_pia_2013:GAINS_MAX_CLE_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2030_LLE1 (energeo_pia_2013:GAINS_MAX_CLE_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2030_LLE2 (energeo_pia_2013:GAINS_MAX_CLE_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2040_LLE1 (energeo_pia_2013:GAINS_MAX_CLE_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2040_LLE2 (energeo_pia_2013:GAINS_MAX_CLE_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2050_LLE1 (energeo_pia_2013:GAINS_MAX_CLE_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_O3_2050_LLE2 (energeo_pia_2013:GAINS_MAX_CLE_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_MAX_CLE_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_MAX_CLE_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_MAX_CLE_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (CLE)

GAINS_MAX_CLE_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_MAX_CLE_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (CLE)

GAINS_MAX_MFR_O3_2005_LLE1 (energeo_pia_2013:GAINS_MAX_MFR_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2005_LLE2 (energeo_pia_2013:GAINS_MAX_MFR_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2030_LLE1 (energeo_pia_2013:GAINS_MAX_MFR_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2030_LLE2 (energeo_pia_2013:GAINS_MAX_MFR_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2040_LLE1 (energeo_pia_2013:GAINS_MAX_MFR_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2040_LLE2 (energeo_pia_2013:GAINS_MAX_MFR_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2050_LLE1 (energeo_pia_2013:GAINS_MAX_MFR_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_O3_2050_LLE2 (energeo_pia_2013:GAINS_MAX_MFR_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_MAX_MFR_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_MAX_MFR_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_MAX_MFR_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (MTFR)

GAINS_MAX_MFR_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_MAX_MFR_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Max. Renewable Energy scenario (MTFR)

GAINS_NUC_CLE_O3_2005_LLE1 (energeo_pia_2013:GAINS_NUC_CLE_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2005_LLE2 (energeo_pia_2013:GAINS_NUC_CLE_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2030_LLE1 (energeo_pia_2013:GAINS_NUC_CLE_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2030_LLE2 (energeo_pia_2013:GAINS_NUC_CLE_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2040_LLE1 (energeo_pia_2013:GAINS_NUC_CLE_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2040_LLE2 (energeo_pia_2013:GAINS_NUC_CLE_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2050_LLE1 (energeo_pia_2013:GAINS_NUC_CLE_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_O3_2050_LLE2 (energeo_pia_2013:GAINS_NUC_CLE_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Island Europe scenario (CLE)

GAINS_NUC_CLE_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_NUC_CLE_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (CLE)

GAINS_NUC_CLE_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_NUC_CLE_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (CLE)

GAINS_NUC_CLE_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_NUC_CLE_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (CLE)

GAINS_NUC_CLE_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_NUC_CLE_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (CLE)

GAINS_NUC_FEF_O3_2005_LLE1 (energeo_pia_2013:GAINS_NUC_FEF_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2005_LLE2 (energeo_pia_2013:GAINS_NUC_FEF_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2030_LLE1 (energeo_pia_2013:GAINS_NUC_FEF_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2030_LLE2 (energeo_pia_2013:GAINS_NUC_FEF_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2040_LLE1 (energeo_pia_2013:GAINS_NUC_FEF_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2040_LLE2 (energeo_pia_2013:GAINS_NUC_FEF_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2050_LLE1 (energeo_pia_2013:GAINS_NUC_FEF_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_O3_2050_LLE2 (energeo_pia_2013:GAINS_NUC_FEF_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Island Europe scenario (FEF)

GAINS_NUC_FEF_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_NUC_FEF_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (FEF)

GAINS_NUC_FEF_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_NUC_FEF_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (FEF)

GAINS_NUC_FEF_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_NUC_FEF_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (FEF)

GAINS_NUC_FEF_PM25_2005_LLE_yoll2 (gn:GAINS_NUC_FEF_PM25_2005_LLE_yoll2)

GAINS_NUC_FEF_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_NUC_FEF_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (FEF)

GAINS_NUC_MFR_O3_2005_LLE1 (energeo_pia_2013:GAINS_NUC_MFR_O3_2005_LLE1)

Premature mortality in 2005 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2005_LLE2 (energeo_pia_2013:GAINS_NUC_MFR_O3_2005_LLE2)

Premature mortality in 2005 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2030_LLE1 (energeo_pia_2013:GAINS_NUC_MFR_O3_2030_LLE1)

Premature mortality in 2030 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2030_LLE2 (energeo_pia_2013:GAINS_NUC_MFR_O3_2030_LLE2)

Premature mortality in 2030 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2040_LLE1 (energeo_pia_2013:GAINS_NUC_MFR_O3_2040_LLE1)

Premature mortality in 2040 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2040_LLE2 (energeo_pia_2013:GAINS_NUC_MFR_O3_2040_LLE2)

Premature mortality in 2040 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2050_LLE1 (energeo_pia_2013:GAINS_NUC_MFR_O3_2050_LLE1)

Premature mortality in 2050 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_O3_2050_LLE2 (energeo_pia_2013:GAINS_NUC_MFR_O3_2050_LLE2)

Premature mortality in 2050 due to Ozone from Island Europe scenario (MTFR)

GAINS_NUC_MFR_PM25_2005_LLE_doll1 (energeo_pia_2013:GAINS_NUC_MFR_PM25_2005_LLE_doll1)

Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (MTFR)

GAINS_NUC_MFR_PM25_2005_LLE_doll2 (energeo_pia_2013:GAINS_NUC_MFR_PM25_2005_LLE_doll2)

National mean Days of Life Loss Expectancy per person above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (MTFR)

GAINS_NUC_MFR_PM25_2005_LLE_yoll1 (energeo_pia_2013:GAINS_NUC_MFR_PM25_2005_LLE_yoll1)

Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (MTFR)

GAINS_NUC_MFR_PM25_2005_LLE_yoll2 (energeo_pia_2013:GAINS_NUC_MFR_PM25_2005_LLE_yoll2)

National mean Years of Life Loss Expectancy for population above 30 years in 2005 due to PM2.5 from GAINS Island Europe scenario (MTFR)

HC1_mean_Gd_1985 (helioclim1:HC1_mean_Gd_1985)

HelioClim-1 Yearly Mean of Irradiance - Year 1985 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1985_2005 (helioclim1:HC1_mean_Gd_1985_2005)

HelioClim-1 Yearly Mean of Irradiance - Years 1985-2005 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1985_EPSG_4326_r32_geo (qc:HC1_mean_Gd_1985_EPSG_4326_r32_geo)

HC1_mean_Gd_1986 (helioclim1:HC1_mean_Gd_1986)

HelioClim-1 Yearly Mean of Irradiance - Year 1986 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1987 (helioclim1:HC1_mean_Gd_1987)

HelioClim-1 Yearly Mean of Irradiance - Year 1987 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1988 (helioclim1:HC1_mean_Gd_1988)

HelioClim-1 Yearly Mean of Irradiance - Year 1988 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1989 (helioclim1:HC1_mean_Gd_1989)

HelioClim-1 Yearly Mean of Irradiance - Year 1989 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1990 (helioclim1:HC1_mean_Gd_1990)

HelioClim-1 Yearly Mean of Irradiance - Year 1990 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1991 (helioclim1:HC1_mean_Gd_1991)

HelioClim-1 Yearly Mean of Irradiance - Year 1991 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1992 (helioclim1:HC1_mean_Gd_1992)

HelioClim-1 Yearly Mean of Irradiance - Year 1992 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1993 (helioclim1:HC1_mean_Gd_1993)

HelioClim-1 Yearly Mean of Irradiance - Year 1993 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1994 (helioclim1:HC1_mean_Gd_1994)

HelioClim-1 Yearly Mean of Irradiance - Year 1994 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1995 (helioclim1:HC1_mean_Gd_1995)

HelioClim-1 Yearly Mean of Irradiance - Year 1995 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1996 (helioclim1:HC1_mean_Gd_1996)

HelioClim-1 Yearly Mean of Irradiance - Year 1996 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1997 (helioclim1:HC1_mean_Gd_1997)

HelioClim-1 Yearly Mean of Irradiance - Year 1997 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1998 (helioclim1:HC1_mean_Gd_1998)

HelioClim-1 Yearly Mean of Irradiance - Year 1998 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_1999 (helioclim1:HC1_mean_Gd_1999)

HelioClim-1 Yearly Mean of Irradiance - Year 1999 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2000 (helioclim1:HC1_mean_Gd_2000)

HelioClim-1 Yearly Mean of Irradiance - Year 2000 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2001 (helioclim1:HC1_mean_Gd_2001)

HelioClim-1 Yearly Mean of Irradiance - Year 2001 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2002 (helioclim1:HC1_mean_Gd_2002)

HelioClim-1 Yearly Mean of Irradiance - Year 2002 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2003 (helioclim1:HC1_mean_Gd_2003)

HelioClim-1 Yearly Mean of Irradiance - Year 2003 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2004 (helioclim1:HC1_mean_Gd_2004)

HelioClim-1 Yearly Mean of Irradiance - Year 2004 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HC1_mean_Gd_2005 (helioclim1:HC1_mean_Gd_2005)

HelioClim-1 Yearly Mean of Irradiance - Year 2005 The HelioClim-1 database, abbreviated in HC-1, offers daily values of Surface Solar Irradiation (SSI) for the period 1985–2005. It has been created from archives of images of the Meteosat First Generation. This dataset provides for each year (1985-2005) a map of yearly mean of irradiance in (w/m2).

HelioClim3v4-MC_BNI_Apr2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Apr2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Apr. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Apr. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Aug2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Aug2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Aug. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Aug. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Dec2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Dec2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Dec. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Dec. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Feb2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Feb2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Feb. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Feb. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Jan2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Jan2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Jan. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Jan. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Jul2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Jul2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Jul. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Jul. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Jun2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Jun2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Jun. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Jun. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Mar2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Mar2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Mar. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Mar. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_May2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_May2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of May. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of May. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Nov2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Nov2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Nov. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Nov. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Oct2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Oct2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Oct. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Oct. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_Sep2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_Sep2005)

HelioClim3v4-MC Monthly Direct Normal Irradiation for the month of Sep. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the month of Sep. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_BNI_year2005 (helioclim3v4mc:HelioClim3v4-MC_BNI_year2005)

HelioClim3v4-MC Yearly Direct Normal Irradiation for the year 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Direct component of the irradiation received by a plane normal to sun rays during the year 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. Yearly irradiation values are computed only for pixels for which 12 monthly irradiation values are available. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Apr2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Apr2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Apr. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Apr. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Aug2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Aug2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Aug. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Aug. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Dec2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Dec2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Dec. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Dec. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Feb2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Feb2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Feb. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Feb. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Jan2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Jan2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Jan. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Jan. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Jul2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Jul2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Jul. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Jul. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Jun2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Jun2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Jun. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Jun. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Mar2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Mar2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Mar. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Mar. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/ :

HelioClim3v4-MC_DHI_May2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_May2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of May. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of May. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Nov2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Nov2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Nov. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Nov. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Oct2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Oct2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Oct. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Oct. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_Sep2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_Sep2005)

HelioClim3v4-MC Monthly Diffuse Horizontal Irradiation for the month of Sep. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the month of Sep. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_DHI_year2005 (helioclim3v4mc:HelioClim3v4-MC_DHI_year2005)

HelioClim3v4-MC Yearly Diffuse Horizontal Irradiation for the year 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Diffuse irradiation received by a horizontal plane during the year 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. Yearly irradiation values are computed only for pixels for which 12 monthly irradiation values are available. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Apr2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Apr2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Apr. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Apr. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Aug2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Aug2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Aug. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Aug. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Dec2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Dec2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Dec. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Dec. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Feb2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Feb2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Feb. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Feb. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Jan2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Jan2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Jan. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Jan. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Jul2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Jul2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Jul. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Jul. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Jun2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Jun2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Jun. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Jun. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Mar2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Mar2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Mar. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Mar. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_May2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_May2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of May. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of May. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Nov2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Nov2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Nov. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Nov. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Oct2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Oct2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Oct. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Oct. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_Sep2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_Sep2005)

HelioClim3v4-MC Monthly Global Horizontal Irradiation for the month of Sep. 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the month of Sep. 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

HelioClim3v4-MC_GHI_year2005 (helioclim3v4mc:HelioClim3v4-MC_GHI_year2005)

HelioClim3v4-MC Yearly Global Horizontal Irradiation for the year 2005 in kWh/m2. Copyright 2013 MINES ParisTech / Transvalor Global irradiation received by a horizontal plane during the year 2005 for the field-of-view of the Meteosat satellite. MINES ParisTech has developed the Heliosat-2 method that converts 15 min Meteosat images into irradiation maps and stores them into the HelioClim3 database. Yearly irradiation values are computed only for pixels for which 12 monthly irradiation values are available. A monthly irradiation value is computed only if at least 25 daily irradiation values are available. The irradiation values of the missing days are computed by taking into account the mean value of the valid days and the length of each missing day. A day is valid if the HelioSat-2 method can be applied on at least one 15 min slot. Gaps in the day are filled by taking into account the available 15 min irradiation values and the length of the day. The other irradiation components (direct, diffuse) received on an horizontal, tilted or normal plane are then computed and provided via the SoDa Service (www.soda-is.com and pro.soda-is.com) since 2003. Such data are used by academics for teaching and research in solar energy, environment, climate and others, and by companies for the sitting of solar plants (PV, CST), their sizing, and the monitoring of their production. Since 2009, the French company Transvalor is in charge of the SoDa Service. Transvalor provides in addition a series of user-tailored services, such as these maps made with MINES ParisTech that combine HelioClim-3 data with an advanced model McClear that estimates the irradiation that should be received for a given site and given instant if the sky were clear, aka clear sky irradiation. Here MC stands for McClear. Transvalor and MINES ParisTech have set up the McClear Clear-Sky Irradiation service that delivers time series of clear sky global, direct, direct normal, and diffuse irradiation for any site in the world, any period of time starting in 2004 up to now, with a time step ranging from 1 min to 1 month. The McClear is an outcome of the MACC and MACC-II EU-funded projects. More Information: Heliosat-2 publication: http://hal.archives-ouvertes.fr/docs/00/36/13/64/PDF/solar_energy04_heliosat2.pdf HelioClim-3: http://www.soda-is.com/eng/helioclim/helioclim3_eng.html McClear publication: http://www.atmos-meas-tech.net/6/2403/2013/amt-6-2403-2013.pdf McClear Web service: http://www.soda-pro.com/free-web-services/radiation/mcclear MACC projects: http://www.gmes-atmosphere.eu/

LLE_2005_PM_GAINS_baseline_doll1 (energeo_pia:LLE_2005_PM_GAINS_baseline_doll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Baseline scenario

LLE_2005_PM_GAINS_baseline_doll2 (energeo_pia:LLE_2005_PM_GAINS_baseline_doll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Baseline scenario

LLE_2005_PM_GAINS_baseline_yoll1 (energeo_pia:LLE_2005_PM_GAINS_baseline_yoll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Baseline scenario

LLE_2005_PM_GAINS_baseline_yoll2 (energeo_pia:LLE_2005_PM_GAINS_baseline_yoll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Baseline scenario

LLE_2005_PM_GAINS_island_europe_doll1 (energeo_pia:LLE_2005_PM_GAINS_island_europe_doll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Island Europe scenario

LLE_2005_PM_GAINS_island_europe_doll2 (energeo_pia:LLE_2005_PM_GAINS_island_europe_doll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Island Europe scenario

LLE_2005_PM_GAINS_island_europe_yoll1 (energeo_pia:LLE_2005_PM_GAINS_island_europe_yoll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Island Europe scenario

LLE_2005_PM_GAINS_island_europe_yoll2 (energeo_pia:LLE_2005_PM_GAINS_island_europe_yoll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Island Europe scenario

LLE_2005_PM_GAINS_max_ren_doll1 (energeo_pia:LLE_2005_PM_GAINS_max_ren_doll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Max renewable power scenario

LLE_2005_PM_GAINS_max_ren_doll2 (energeo_pia:LLE_2005_PM_GAINS_max_ren_doll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Max renewable power scenario

LLE_2005_PM_GAINS_max_ren_yoll1 (energeo_pia:LLE_2005_PM_GAINS_max_ren_yoll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Max renewable power scenario

LLE_2005_PM_GAINS_max_ren_yoll2 (energeo_pia:LLE_2005_PM_GAINS_max_ren_yoll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Max renewable power scenario

LLE_2005_PM_GAINS_open_europe_doll1 (energeo_pia:LLE_2005_PM_GAINS_open_europe_doll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Open Europe scenario

LLE_2005_PM_GAINS_open_europe_doll2 (energeo_pia:LLE_2005_PM_GAINS_open_europe_doll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Open Europe scenario

LLE_2005_PM_GAINS_open_europe_yoll1 (energeo_pia:LLE_2005_PM_GAINS_open_europe_yoll1)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Open Europe scenario

LLE_2005_PM_GAINS_open_europe_yoll2 (energeo_pia:LLE_2005_PM_GAINS_open_europe_yoll2)

Loss of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS Open Europe scenario

LLE_vs_GAINS_baseline_c00_doll (energeo_pia:LLE_vs_GAINS_baseline_c00_doll)

Lost of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c00_yoll (energeo_pia:LLE_vs_GAINS_baseline_c00_yoll)

Lost of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c01_doll (energeo_pia:LLE_vs_GAINS_baseline_c01_doll)

Lost of Life Expectancy for cohort 30-34 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c01_yoll (energeo_pia:LLE_vs_GAINS_baseline_c01_yoll)

Lost of Life Expectancy for cohort 30-34 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c02_doll (energeo_pia:LLE_vs_GAINS_baseline_c02_doll)

Lost of Life Expectancy for cohort 35-39 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c02_yoll (energeo_pia:LLE_vs_GAINS_baseline_c02_yoll)

Lost of Life Expectancy for cohort 35-39 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c03_doll (energeo_pia:LLE_vs_GAINS_baseline_c03_doll)

Lost of Life Expectancy for cohort 40-44 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c03_yoll (energeo_pia:LLE_vs_GAINS_baseline_c03_yoll)

Lost of Life Expectancy for cohort 40-44 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c04_doll (energeo_pia:LLE_vs_GAINS_baseline_c04_doll)

Lost of Life Expectancy for cohort 45-49 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c04_yoll (energeo_pia:LLE_vs_GAINS_baseline_c04_yoll)

Lost of Life Expectancy for cohort 45-49 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c05_doll (energeo_pia:LLE_vs_GAINS_baseline_c05_doll)

Lost of Life Expectancy for cohort 50-54 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c05_yoll (energeo_pia:LLE_vs_GAINS_baseline_c05_yoll)

Lost of Life Expectancy for cohort 50-54 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c06_doll (energeo_pia:LLE_vs_GAINS_baseline_c06_doll)

Lost of Life Expectancy for cohort 55-59 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c06_yoll (energeo_pia:LLE_vs_GAINS_baseline_c06_yoll)

Lost of Life Expectancy for cohort 55-59 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c07_doll (energeo_pia:LLE_vs_GAINS_baseline_c07_doll)

Lost of Life Expectancy for cohort 60-64 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c07_yoll (energeo_pia:LLE_vs_GAINS_baseline_c07_yoll)

Lost of Life Expectancy for cohort 60-64 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c08_doll (energeo_pia:LLE_vs_GAINS_baseline_c08_doll)

Lost of Life Expectancy for cohort 65-69 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c08_yoll (energeo_pia:LLE_vs_GAINS_baseline_c08_yoll)

Lost of Life Expectancy for cohort 65-69 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c09_doll (energeo_pia:LLE_vs_GAINS_baseline_c09_doll)

Lost of Life Expectancy for cohort 70-74 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c09_yoll (energeo_pia:LLE_vs_GAINS_baseline_c09_yoll)

Lost of Life Expectancy for cohort 70-74 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c10_doll (energeo_pia:LLE_vs_GAINS_baseline_c10_doll)

Lost of Life Expectancy for cohort 75-79 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c10_yoll (energeo_pia:LLE_vs_GAINS_baseline_c10_yoll)

Lost of Life Expectancy for cohort 75-79 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c11_doll (energeo_pia:LLE_vs_GAINS_baseline_c11_doll)

Lost of Life Expectancy for cohort 80-84 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c11_yoll (energeo_pia:LLE_vs_GAINS_baseline_c11_yoll)

Lost of Life Expectancy for cohort 80-84 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c12_doll (energeo_pia:LLE_vs_GAINS_baseline_c12_doll)

Lost of Life Expectancy for cohort 85-89 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c12_yoll (energeo_pia:LLE_vs_GAINS_baseline_c12_yoll)

Lost of Life Expectancy for cohort 85-89 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c13_doll (energeo_pia:LLE_vs_GAINS_baseline_c13_doll)

Lost of Life Expectancy for cohort 90-94 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c13_yoll (energeo_pia:LLE_vs_GAINS_baseline_c13_yoll)

Lost of Life Expectancy for cohort 90-94 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c14_doll (energeo_pia:LLE_vs_GAINS_baseline_c14_doll)

Lost of Life Expectancy for cohort 95-99 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c14_yoll (energeo_pia:LLE_vs_GAINS_baseline_c14_yoll)

Lost of Life Expectancy for cohort 95-99 years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c15_doll (energeo_pia:LLE_vs_GAINS_baseline_c15_doll)

Lost of Life Expectancy for cohort 100+ years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_GAINS_baseline_c15_yoll (energeo_pia:LLE_vs_GAINS_baseline_c15_yoll)

Lost of Life Expectancy for cohort 100+ years in 2005 due to PM 2.5 from GAINS baseline scenario

LLE_vs_TNO_2008_layer_08 (energeo:LLE_vs_TNO_2008_layer_08)

LLE_vs_TNO_layer_27_01_doll (energeo_pia:LLE_vs_TNO_layer_27_01_doll)

Lost of Life Expectancy for cohort 30-34 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c00_doll (energeo_pia:LLE_vs_TNO_layer_27_c00_doll)

Lost of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c00_yoll (energeo_pia:LLE_vs_TNO_layer_27_c00_yoll)

Lost of Life Expectancy for population over 30 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c01_doll (energeo_pia:LLE_vs_TNO_layer_27_c01_doll)

Lost of Life Expectancy for cohort 30-34 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c01_yoll (energeo_pia:LLE_vs_TNO_layer_27_c01_yoll)

Lost of Life Expectancy for cohort 30-34 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c02_doll (energeo_pia:LLE_vs_TNO_layer_27_c02_doll)

Lost of Life Expectancy for cohort 35-39 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c02_yoll (energeo_pia:LLE_vs_TNO_layer_27_c02_yoll)

Lost of Life Expectancy for cohort 35-39 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c03_doll (energeo_pia:LLE_vs_TNO_layer_27_c03_doll)

Lost of Life Expectancy for cohort 40-44 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c03_yoll (energeo_pia:LLE_vs_TNO_layer_27_c03_yoll)

Lost of Life Expectancy for cohort 40-44 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c04_doll (energeo_pia:LLE_vs_TNO_layer_27_c04_doll)

Lost of Life Expectancy for cohort 45-49 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c04_yoll (energeo_pia:LLE_vs_TNO_layer_27_c04_yoll)

Lost of Life Expectancy for cohort 45-49 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c05_doll (energeo_pia:LLE_vs_TNO_layer_27_c05_doll)

Lost of Life Expectancy for cohort 50-54 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c05_yoll (energeo_pia:LLE_vs_TNO_layer_27_c05_yoll)

Lost of Life Expectancy for cohort 50-54 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c06_doll (energeo_pia:LLE_vs_TNO_layer_27_c06_doll)

Lost of Life Expectancy for cohort 55-59 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c06_yoll (energeo_pia:LLE_vs_TNO_layer_27_c06_yoll)

Lost of Life Expectancy for cohort 55-59 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c07_doll (energeo_pia:LLE_vs_TNO_layer_27_c07_doll)

Lost of Life Expectancy for cohort 60-64 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c07_yoll (energeo_pia:LLE_vs_TNO_layer_27_c07_yoll)

Lost of Life Expectancy for cohort 60-64 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c08_doll (energeo_pia:LLE_vs_TNO_layer_27_c08_doll)

Lost of Life Expectancy for cohort 65-69 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c08_yoll (energeo_pia:LLE_vs_TNO_layer_27_c08_yoll)

Lost of Life Expectancy for cohort 65-69 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c09_doll (energeo_pia:LLE_vs_TNO_layer_27_c09_doll)

Lost of Life Expectancy for cohort 70-74 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c09_yoll (energeo_pia:LLE_vs_TNO_layer_27_c09_yoll)

Lost of Life Expectancy for cohort 70-74 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c10_doll (energeo_pia:LLE_vs_TNO_layer_27_c10_doll)

Lost of Life Expectancy for cohort 75-79 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c10_yoll (energeo_pia:LLE_vs_TNO_layer_27_c10_yoll)

Lost of Life Expectancy for cohort 75-79 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c11_doll (energeo_pia:LLE_vs_TNO_layer_27_c11_doll)

Lost of Life Expectancy for cohort 80-84 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c11_yoll (energeo_pia:LLE_vs_TNO_layer_27_c11_yoll)

Lost of Life Expectancy for cohort 80-84 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c12_doll (energeo_pia:LLE_vs_TNO_layer_27_c12_doll)

Lost of Life Expectancy for cohort 85-89 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c12_yoll (energeo_pia:LLE_vs_TNO_layer_27_c12_yoll)

Lost of Life Expectancy for cohort 85-89 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c13_doll (energeo_pia:LLE_vs_TNO_layer_27_c13_doll)

Lost of Life Expectancy for cohort 90-94 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c13_yoll (energeo_pia:LLE_vs_TNO_layer_27_c13_yoll)

Lost of Life Expectancy for cohort 90-94 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c14_doll (energeo_pia:LLE_vs_TNO_layer_27_c14_doll)

Lost of Life Expectancy for cohort 95-99 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c14_yoll (energeo_pia:LLE_vs_TNO_layer_27_c14_yoll)

Lost of Life Expectancy for cohort 95-99 years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c15_doll (energeo_pia:LLE_vs_TNO_layer_27_c15_doll)

Lost of Life Expectancy for cohort 100+ years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

LLE_vs_TNO_layer_27_c15_yoll (energeo_pia:LLE_vs_TNO_layer_27_c15_yoll)

Lost of Life Expectancy for cohort 100+ years in 2005 due to PM 2.5 from Total PM 2.5 (TNO 2008)

Marine Energy Resource Characterization (marine:Marine_Energy_Resource_Characterization)

Information relevant for the characterization of marine energy resource has been produced based on Homere sea state hindcast (Boudière et al. 2013, Boudière et al. 2014). The information reaches spatial resolution down to 200 m near the coast and focuses on the availability and spatial/temporal variability of the resource. The parameters are : Hs : Significant wave height (m), Te : Energy period (s), CgE : Wave power density (kW/m), fPeak : Wave peak frequency (Hz) For any computational grid point, annual and seasonal averages of these parameters are displayed within each row of the table. Also, dedicated links deliver synthetic information about wave climate and characterization of weather windows. Further information will be added as the current project goes on. References E. Boudière, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, “A suitable metocean hindcast database for the design of Marine energy converters,” International Journal of Marine Energy, vol. 3–4, pp. e40–e52, Dec. 2013. E. Boudière and C. Maisondieu, “Manuel de l’utilisateur de la base de données HOMERE,” 2014.

Merra-2012-SHP (merra:Merra-2012-SHP)

R_tilt00deg_azim0deg (aip3map:R_tilt00deg_azim0deg)

R_tilt00deg_azim15degE (aip3map:R_tilt00deg_azim15degE)

R_tilt00deg_azim15degO (aip3map:R_tilt00deg_azim15degO)

R_tilt00deg_azim30degE (aip3map:R_tilt00deg_azim30degE)

R_tilt00deg_azim30degO (aip3map:R_tilt00deg_azim30degO)

R_tilt00deg_azim45degE (aip3map:R_tilt00deg_azim45degE)

R_tilt00deg_azim45degO (aip3map:R_tilt00deg_azim45degO)

R_tilt00deg_azim90degE (aip3map:R_tilt00deg_azim90degE)

R_tilt00deg_azim90degO (aip3map:R_tilt00deg_azim90degO)

R_tilt30deg_azim0deg (aip3map:R_tilt30deg_azim0deg)

R_tilt30deg_azim15degE (aip3map:R_tilt30deg_azim15degE)

R_tilt30deg_azim15degO (aip3map:R_tilt30deg_azim15degO)

R_tilt30deg_azim30degE (aip3map:R_tilt30deg_azim30degE)

R_tilt30deg_azim30degO (aip3map:R_tilt30deg_azim30degO)

R_tilt30deg_azim45degE (aip3map:R_tilt30deg_azim45degE)

R_tilt30deg_azim45degO (aip3map:R_tilt30deg_azim45degO)

R_tilt30deg_azim90degE (aip3map:R_tilt30deg_azim90degE)

R_tilt30deg_azim90degO (aip3map:R_tilt30deg_azim90degO)

R_tilt45deg_azim0deg (aip3map:R_tilt45deg_azim0deg)

R_tilt45deg_azim15degE (aip3map:R_tilt45deg_azim15degE)

R_tilt45deg_azim15degO (aip3map:R_tilt45deg_azim15degO)

R_tilt45deg_azim30degE (aip3map:R_tilt45deg_azim30degE)

R_tilt45deg_azim30degO (aip3map:R_tilt45deg_azim30degO)

R_tilt45deg_azim45degE (aip3map:R_tilt45deg_azim45degE)

R_tilt45deg_azim45degO (aip3map:R_tilt45deg_azim45degO)

R_tilt45deg_azim90degE (aip3map:R_tilt45deg_azim90degE)

R_tilt45deg_azim90degO (aip3map:R_tilt45deg_azim90degO)

R_tilt60deg_azim0deg (aip3map:R_tilt60deg_azim0deg)

R_tilt60deg_azim15degE (aip3map:R_tilt60deg_azim15degE)

R_tilt60deg_azim15degO (aip3map:R_tilt60deg_azim15degO)

R_tilt60deg_azim30degE (aip3map:R_tilt60deg_azim30degE)

R_tilt60deg_azim30degO (aip3map:R_tilt60deg_azim30degO)

R_tilt60deg_azim45degE (aip3map:R_tilt60deg_azim45degE)

R_tilt60deg_azim45degO (aip3map:R_tilt60deg_azim45degO)

R_tilt60deg_azim90degE (aip3map:R_tilt60deg_azim90degE)

R_tilt60deg_azim90degO (aip3map:R_tilt60deg_azim90degO)

R_tilt90deg_azim0deg (aip3map:R_tilt90deg_azim0deg)

R_tilt90deg_azim15degE (aip3map:R_tilt90deg_azim15degE)

R_tilt90deg_azim15degO (aip3map:R_tilt90deg_azim15degO)

R_tilt90deg_azim30degE (aip3map:R_tilt90deg_azim30degE)

R_tilt90deg_azim30degO (aip3map:R_tilt90deg_azim30degO)

R_tilt90deg_azim45degE (aip3map:R_tilt90deg_azim45degE)

R_tilt90deg_azim45degO (aip3map:R_tilt90deg_azim45degO)

R_tilt90deg_azim90degE (aip3map:R_tilt90deg_azim90degE)

R_tilt90deg_azim90degO (aip3map:R_tilt90deg_azim90degO)

Total_PM_2.5_from_TNO_2008 (energeo:Total_PM_2.5_from_TNO_2008)

Lost of life expectancy for cohorts 30 to 95 years old. Total PM 2.5 from TNO 2008

WindIntensity1985 (merra:WindIntensity1985)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1986 (merra:WindIntensity1986)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1987 (merra:WindIntensity1987)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1988 (merra:WindIntensity1988)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1989 (merra:WindIntensity1989)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1990 (merra:WindIntensity1990)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1991 (merra:WindIntensity1991)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1992 (merra:WindIntensity1992)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1993 (merra:WindIntensity1993)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1994 (merra:WindIntensity1994)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1995 (merra:WindIntensity1995)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1996 (merra:WindIntensity1996)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1997 (merra:WindIntensity1997)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1998 (merra:WindIntensity1998)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity1999 (merra:WindIntensity1999)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2000 (merra:WindIntensity2000)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2001 (merra:WindIntensity2001)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2002 (merra:WindIntensity2002)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2003 (merra:WindIntensity2003)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2004 (merra:WindIntensity2004)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2005 (merra:WindIntensity2005)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2006 (merra:WindIntensity2006)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2007 (merra:WindIntensity2007)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2008 (merra:WindIntensity2008)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2009 (merra:WindIntensity2009)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2010 (merra:WindIntensity2010)

global 10-m wind intensity from MERRA for the entire 1985 year

WindIntensity2011 (merra:WindIntensity2011)

global 10-m wind intensity from MERRA for the entire 1985 year

_tmp__cc_14_60_lOM_bfond_hFR_Lo (energeo_tmp_cc:_tmp__cc_14_60_lOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__cc_1_20_1_gravity_0_Lo (energeo_tmp_cc:_tmp__cc_1_20_1_gravity_0_Lo)

Wind Farm Impact

_tmp__cc_20_10_hOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_20_10_hOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__cc_20_40_hOM_bfond_hFR_Lo (energeo_tmp_cc:_tmp__cc_20_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__cc_20_40_hOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_20_40_hOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__cc_20_40_lOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_20_40_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__cc_20_46_hOM_bfond_hFR_Lo (energeo_tmp_cc:_tmp__cc_20_46_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__cc_21_46_hOM_bfond_hFR_Lo (energeo_tmp_cc:_tmp__cc_21_46_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__cc_21_46_lOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_21_46_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__cc_25_36_lOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_25_36_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__cc_30_30_lOM_bfond_lFR_Lo (energeo_tmp_cc:_tmp__cc_30_30_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__daly_20_40_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_20_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_20_40_hOM_bfond_lFR_Lo (energeo_tmp_daly:_tmp__daly_20_40_hOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__daly_20_40_lOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_20_40_lOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_20_40_lOM_bfond_lFR_Lo (energeo_tmp_daly:_tmp__daly_20_40_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__daly_20_45_lOM_bfond_lFR_Lo (energeo_tmp_daly:_tmp__daly_20_45_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__daly_20_5_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_20_5_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_20_70_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_20_70_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_21_39_lOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_21_39_lOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_21_40_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_21_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_21_40_hOM_bfond_lFR_Lo (energeo_tmp_daly:_tmp__daly_21_40_hOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__daly_22_40_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_22_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__daly_23_40_hOM_bfond_hFR_Lo (energeo_tmp_daly:_tmp__daly_23_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__eco_20_40_hOM_bfond_hFR_Lo (energeo_tmp_eco:_tmp__eco_20_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__eco_20_40_lOM_bfond_lFR_Lo (energeo_tmp_eco:_tmp__eco_20_40_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__eco_20_43_hOM_bfond_hFR_Lo (energeo_tmp_eco:_tmp__eco_20_43_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__eco_21_40_hOM_bfond_hFR_Lo (energeo_tmp_eco:_tmp__eco_21_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__eco_23_42_hOM_bfond_hFR_Lo (energeo_tmp_eco:_tmp__eco_23_42_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__eco_25_41_hOM_bfond_hFR_Lo (energeo_tmp_eco:_tmp__eco_25_41_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__res_20_40_hOM_bfond_hFR_Lo (energeo_tmp_res:_tmp__res_20_40_hOM_bfond_hFR_Lo)

Wind Farm Impact

_tmp__res_20_40_hOM_bfond_lFR_Lo (energeo_tmp_res:_tmp__res_20_40_hOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__res_20_40_lOM_bfond_lFR_Lo (energeo_tmp_res:_tmp__res_20_40_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__res_20_99_lOM_bfond_lFR_Lo (energeo_tmp_res:_tmp__res_20_99_lOM_bfond_lFR_Lo)

Wind Farm Impact

_tmp__res_28_5_hOM_bfond_lFR_Lo (energeo_tmp_res:_tmp__res_28_5_hOM_bfond_lFR_Lo)

Wind Farm Impact

australia_january_epsg4326 (australia:australia_january_epsg4326)

bom_solar_an (bom_solar:bom_solar_an)

Average daily solar exposure annual. Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_apr (bom_solar:bom_solar_apr)

Average daily solar exposure April. Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_aug (bom_solar:bom_solar_aug)

Average daily solar exposure August Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_aut (bom_solar:bom_solar_aut)

Average daily solar exposure autumn Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_dec (bom_solar:bom_solar_dec)

Average daily solar exposure December Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_dry (bom_solar:bom_solar_dry)

Average daily solar exposure May to September (Northern dry season) Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_feb (bom_solar:bom_solar_feb)

Average daily solar exposure February Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_jan (bom_solar:bom_solar_jan)

Average daily solar exposure January Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_jul (bom_solar:bom_solar_jul)

Average daily solar exposure July Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_jun (bom_solar:bom_solar_jun)

Average daily solar exposure June Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_mar (bom_solar:bom_solar_mar)

Average daily solar exposure March Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_may (bom_solar:bom_solar_may)

Average daily solar exposure May Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_nov (bom_solar:bom_solar_nov)

Average daily solar exposure November Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_oct (bom_solar:bom_solar_oct)

Average daily solar exposure October Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_sep (bom_solar:bom_solar_sep)

Average daily solar exposure September Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_spr (bom_solar:bom_solar_spr)

Average daily solar exposure spring Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_sum (bom_solar:bom_solar_sum)

Average daily solar exposure summer Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_wet (bom_solar:bom_solar_wet)

Average daily solar exposure October to April (Northern wet season) Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

bom_solar_win (bom_solar:bom_solar_win)

Average daily solar exposure winter Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day, and is typically between 1 and 35 MJ/m2 (megajoules per square metre). The Bureau of Meteorology's (BOM) computer radiation model uses visible images from geostationary meteorological satellites to estimate daily global solar exposures at ground level. At each location the image brightness is used to provide an estimate of the solar irradiance at the ground. Essentially, the irradiance at the ground can be calculated from the irradiance at the top of the earth's atmosphere, the amount absorbed in the atmosphere (dependant on the amount of water vapour present), the amount reflected from the surface (surface albedo) and the amount reflected from clouds (cloud albedo). These instantaneous irradiance values are integrated over the day to give daily solar exposure in megajoules per square metre. More information: http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp?period=an#maps Access constraints: http://www.bom.gov.au/climate/averages/climatology/solar_radiation/average-solar-exposure-metadata.pdf

cc_15_30_hOM_bfond_hFR_Lo (energeo:cc_15_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_30_hOM_bfond_lFR_Lo (energeo:cc_15_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_30_lOM_bfond_hFR_Lo (energeo:cc_15_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_30_lOM_bfond_lFR_Lo (energeo:cc_15_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_40_hOM_bfond_hFR_Lo (energeo:cc_15_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_40_hOM_bfond_lFR_Lo (energeo:cc_15_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_40_lOM_bfond_hFR_Lo (energeo:cc_15_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_40_lOM_bfond_lFR_Lo (energeo:cc_15_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_50_hOM_bfond_hFR_Lo (energeo:cc_15_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_50_hOM_bfond_lFR_Lo (energeo:cc_15_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_50_lOM_bfond_hFR_Lo (energeo:cc_15_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_15_50_lOM_bfond_lFR_Lo (energeo:cc_15_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_30_hOM_bfond_hFR_Lo (energeo:cc_20_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_30_hOM_bfond_lFR_Lo (energeo:cc_20_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_30_lOM_bfond_hFR_Lo (energeo:cc_20_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_30_lOM_bfond_lFR_Lo (energeo:cc_20_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_40_hOM_bfond_hFR_Lo (energeo:cc_20_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_40_hOM_bfond_lFR_Lo (energeo:cc_20_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_40_lOM_bfond_hFR_Lo (energeo:cc_20_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_40_lOM_bfond_lFR_Lo (energeo:cc_20_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_50_hOM_bfond_hFR_Lo (energeo:cc_20_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_50_hOM_bfond_lFR_Lo (energeo:cc_20_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_50_lOM_bfond_hFR_Lo (energeo:cc_20_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_20_50_lOM_bfond_lFR_Lo (energeo:cc_20_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_30_hOM_bfond_hFR_Lo (energeo:cc_25_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_30_hOM_bfond_lFR_Lo (energeo:cc_25_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_30_lOM_bfond_hFR_Lo (energeo:cc_25_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_30_lOM_bfond_lFR_Lo (energeo:cc_25_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_40_hOM_bfond_hFR_Lo (energeo:cc_25_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_40_hOM_bfond_lFR_Lo (energeo:cc_25_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_40_lOM_bfond_hFR_Lo (energeo:cc_25_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_40_lOM_bfond_lFR_Lo (energeo:cc_25_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_50_hOM_bfond_hFR_Lo (energeo:cc_25_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_50_hOM_bfond_lFR_Lo (energeo:cc_25_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_50_lOM_bfond_hFR_Lo (energeo:cc_25_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

cc_25_50_lOM_bfond_lFR_Lo (energeo:cc_25_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

coastlines (osm:coastlines)

OpenStreetMap coastlines from (http://openstreetmapdata.com/data/coastlines)

daily_irradiation_dni_year (tmp:daily_irradiation_dni_year)

daily_irradiation_ghi_2005_october (tmp:daily_irradiation_ghi_2005_october)

daly_15_30_hOM_bfond_hFR_Lo (energeo:daly_15_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_30_hOM_bfond_lFR_Lo (energeo:daly_15_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_30_lOM_bfond_hFR_Lo (energeo:daly_15_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_30_lOM_bfond_lFR_Lo (energeo:daly_15_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_40_hOM_bfond_hFR_Lo (energeo:daly_15_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_40_hOM_bfond_lFR_Lo (energeo:daly_15_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_40_lOM_bfond_hFR_Lo (energeo:daly_15_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_40_lOM_bfond_lFR_Lo (energeo:daly_15_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_50_hOM_bfond_hFR_Lo (energeo:daly_15_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_50_hOM_bfond_lFR_Lo (energeo:daly_15_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_50_lOM_bfond_hFR_Lo (energeo:daly_15_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_15_50_lOM_bfond_lFR_Lo (energeo:daly_15_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_30_hOM_bfond_hFR_Lo (energeo:daly_20_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_30_hOM_bfond_lFR_Lo (energeo:daly_20_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_30_lOM_bfond_hFR_Lo (energeo:daly_20_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_30_lOM_bfond_lFR_Lo (energeo:daly_20_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_40_hOM_bfond_hFR_Lo (energeo:daly_20_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_40_hOM_bfond_lFR_Lo (energeo:daly_20_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_40_lOM_bfond_hFR_Lo (energeo:daly_20_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_40_lOM_bfond_lFR_Lo (energeo:daly_20_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_50_hOM_bfond_hFR_Lo (energeo:daly_20_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_50_hOM_bfond_lFR_Lo (energeo:daly_20_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_50_lOM_bfond_hFR_Lo (energeo:daly_20_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_20_50_lOM_bfond_lFR_Lo (energeo:daly_20_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_30_hOM_bfond_hFR_Lo (energeo:daly_25_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_30_hOM_bfond_lFR_Lo (energeo:daly_25_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_30_lOM_bfond_hFR_Lo (energeo:daly_25_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_30_lOM_bfond_lFR_Lo (energeo:daly_25_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_40_hOM_bfond_hFR_Lo (energeo:daly_25_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_40_hOM_bfond_lFR_Lo (energeo:daly_25_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_40_lOM_bfond_hFR_Lo (energeo:daly_25_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_40_lOM_bfond_lFR_Lo (energeo:daly_25_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_50_hOM_bfond_hFR_Lo (energeo:daly_25_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_50_hOM_bfond_lFR_Lo (energeo:daly_25_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_50_lOM_bfond_hFR_Lo (energeo:daly_25_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

daly_25_50_lOM_bfond_lFR_Lo (energeo:daly_25_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

DIVA-GIS countries (gn:diva_gis_countries)

source: http://www.diva-gis.org/Data

eco_15_30_hOM_bfond_hFR_Lo (energeo:eco_15_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_30_hOM_bfond_lFR_Lo (energeo:eco_15_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_30_lOM_bfond_hFR_Lo (energeo:eco_15_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_30_lOM_bfond_lFR_Lo (energeo:eco_15_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_40_hOM_bfond_hFR_Lo (energeo:eco_15_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_40_hOM_bfond_lFR_Lo (energeo:eco_15_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_40_lOM_bfond_hFR_Lo (energeo:eco_15_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_40_lOM_bfond_lFR_Lo (energeo:eco_15_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_50_hOM_bfond_hFR_Lo (energeo:eco_15_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_50_hOM_bfond_lFR_Lo (energeo:eco_15_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_50_lOM_bfond_hFR_Lo (energeo:eco_15_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_15_50_lOM_bfond_lFR_Lo (energeo:eco_15_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_30_hOM_bfond_hFR_Lo (energeo:eco_20_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_30_hOM_bfond_lFR_Lo (energeo:eco_20_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_30_lOM_bfond_hFR_Lo (energeo:eco_20_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_30_lOM_bfond_lFR_Lo (energeo:eco_20_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_40_hOM_bfond_hFR_Lo (energeo:eco_20_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_40_hOM_bfond_lFR_Lo (energeo:eco_20_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_40_lOM_bfond_hFR_Lo (energeo:eco_20_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_40_lOM_bfond_lFR_Lo (energeo:eco_20_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_50_hOM_bfond_hFR_Lo (energeo:eco_20_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_50_hOM_bfond_lFR_Lo (energeo:eco_20_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_50_lOM_bfond_hFR_Lo (energeo:eco_20_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_20_50_lOM_bfond_lFR_Lo (energeo:eco_20_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_30_hOM_bfond_hFR_Lo (energeo:eco_25_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_30_hOM_bfond_lFR_Lo (energeo:eco_25_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_30_lOM_bfond_hFR_Lo (energeo:eco_25_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_30_lOM_bfond_lFR_Lo (energeo:eco_25_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_40_hOM_bfond_hFR_Lo (energeo:eco_25_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_40_hOM_bfond_lFR_Lo (energeo:eco_25_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_40_lOM_bfond_hFR_Lo (energeo:eco_25_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_40_lOM_bfond_lFR_Lo (energeo:eco_25_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_50_hOM_bfond_hFR_Lo (energeo:eco_25_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_50_hOM_bfond_lFR_Lo (energeo:eco_25_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_50_lOM_bfond_hFR_Lo (energeo:eco_25_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

eco_25_50_lOM_bfond_lFR_Lo (energeo:eco_25_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

Environmental performances associated to offshore wind energy generation in Northern Europe (energeo:environmental_performances_of_offshore_wind_energy)

This Web Map Service provides maps of site-specific environmental performances associated to offshore wind energy generation in Northern Europe. These maps have been generated from dedicated algorithm combining Life Cycle Assessment (LCA) results with technical parameters and geo-dependent data having an influence on: foundation choice criteria (water depth), on electricity generation potentials (turbine power curve, wind speed distribution), on transport impact (distance to the nearest harbor), on transmission cables length (distance to shore). Final environmental impacts categories are assessed from the method IMPACT2002+focusing on climate change, resources depletion, human health and ecosystem quality.

gboundaries (gn:gboundaries)

hc3map_monthly_avg_dni (europa:hc3map_monthly_avg_dni)

The HC3Map Web Service provides maps of 5-years average of the monthly irradiation, i.e., the mean energy received during a whole month per square meter. Irradiation is expressed in kWh/m2. Period is 2004-2008. Two maps are provided: one for the global irradiation received on a horizontal plane (GHI), the other for the direct irradiation received on a plane always facing the sun (DNI). These maps are computed from irradiances of the HelioClim 3 database. Provider: Ecole des Mines de Paris / Armines (France).

hc3map_monthly_avg_ghi (europa:hc3map_monthly_avg_ghi)

The HC3Map Web Service provides maps of 5-years average of the monthly irradiation, i.e., the mean energy received during a whole month per square meter. Irradiation is expressed in kWh/m2. Period is 2004-2008. Two maps are provided: one for the global irradiation received on a horizontal plane (GHI), the other for the direct irradiation received on a plane always facing the sun (DNI). These maps are computed from irradiances of the HelioClim 3 database. Provider: Ecole des Mines de Paris / Armines (France).

merra2012PostGIS (merra:merra2012PostGIS)

merra_wind_speed (merra_wind:merra_wind_speed)

Global wind intensity at 10 meters in meter per second (m/s) from MERRA for each year between 1985 year 2012 and the global average. MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The Project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. More information, including an FAQ and the file specification document that explains each of the products in detail, is available at the MERRA web site at the GMAO (http://gmao.gsfc.nasa.gov/merra/). The "Wind intensity" field in the catalog is an annual mean of the global 10-m wind field produced each 6 h at the native resolution of 0.5° in latitude x 0.625° in latitude. Annual mean values are available for each year from 1985 to 2012 as well as the global average for that period.

merra_year_1985 (merra_full:merra_year_1985)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1986 (merra_full:merra_year_1986)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1987 (merra_full:merra_year_1987)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1988 (merra_full:merra_year_1988)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1989 (merra_full:merra_year_1989)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1990 (merra_full:merra_year_1990)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1991 (merra_full:merra_year_1991)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1992 (merra_full:merra_year_1992)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1993 (merra_full:merra_year_1993)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1994 (merra_full:merra_year_1994)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1995 (merra_full:merra_year_1995)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1996 (merra_full:merra_year_1996)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1997 (merra_full:merra_year_1997)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1998 (merra_full:merra_year_1998)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_1999 (merra_full:merra_year_1999)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2000 (merra_full:merra_year_2000)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2001 (merra_full:merra_year_2001)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2002 (merra_full:merra_year_2002)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2003 (merra_full:merra_year_2003)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2004 (merra_full:merra_year_2004)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2005 (merra_full:merra_year_2005)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2006 (merra_full:merra_year_2006)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2007 (merra_full:merra_year_2007)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2008 (merra_full:merra_year_2008)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2009 (merra_full:merra_year_2009)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2010 (merra_full:merra_year_2010)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2011 (merra_full:merra_year_2011)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

merra_year_2012 (merra_full:merra_year_2012)

The shapefile includes yearly mean fields from MERRA. Fields included in the file are: Latitude and longitude in decimal degree. t2m_k: 2-m-temperature in Kelvin ps_pa: Surface pressure in Pa. rh_percent: Relative humidity in %. tdp_k: dewpoint temperature in Kelvin ws10_mpers: wind speed at 10 m in m/s. dir10_deg : wind direction at 10 m in degree.

res_15_30_hOM_bfond_hFR_Lo (energeo:res_15_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_30_hOM_bfond_lFR_Lo (energeo:res_15_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_30_lOM_bfond_hFR_Lo (energeo:res_15_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_30_lOM_bfond_lFR_Lo (energeo:res_15_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_40_hOM_bfond_hFR_Lo (energeo:res_15_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_40_hOM_bfond_lFR_Lo (energeo:res_15_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_40_lOM_bfond_hFR_Lo (energeo:res_15_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_40_lOM_bfond_lFR_Lo (energeo:res_15_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_50_hOM_bfond_hFR_Lo (energeo:res_15_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_50_hOM_bfond_lFR_Lo (energeo:res_15_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_50_lOM_bfond_hFR_Lo (energeo:res_15_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_15_50_lOM_bfond_lFR_Lo (energeo:res_15_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_30_hOM_bfond_hFR_Lo (energeo:res_20_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_30_hOM_bfond_lFR_Lo (energeo:res_20_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_30_lOM_bfond_hFR_Lo (energeo:res_20_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_30_lOM_bfond_lFR_Lo (energeo:res_20_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_40_hOM_bfond_hFR_Lo (energeo:res_20_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_40_hOM_bfond_lFR_Lo (energeo:res_20_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_40_lOM_bfond_hFR_Lo (energeo:res_20_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_40_lOM_bfond_lFR_Lo (energeo:res_20_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_50_hOM_bfond_hFR_Lo (energeo:res_20_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_50_hOM_bfond_lFR_Lo (energeo:res_20_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_50_lOM_bfond_hFR_Lo (energeo:res_20_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_20_50_lOM_bfond_lFR_Lo (energeo:res_20_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_30_hOM_bfond_hFR_Lo (energeo:res_25_30_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_30_hOM_bfond_lFR_Lo (energeo:res_25_30_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_30_lOM_bfond_hFR_Lo (energeo:res_25_30_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_30_lOM_bfond_lFR_Lo (energeo:res_25_30_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_40_hOM_bfond_hFR_Lo (energeo:res_25_40_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_40_hOM_bfond_lFR_Lo (energeo:res_25_40_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_40_lOM_bfond_hFR_Lo (energeo:res_25_40_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_40_lOM_bfond_lFR_Lo (energeo:res_25_40_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_50_hOM_bfond_hFR_Lo (energeo:res_25_50_hOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_50_hOM_bfond_lFR_Lo (energeo:res_25_50_hOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_50_lOM_bfond_hFR_Lo (energeo:res_25_50_lOM_bfond_hFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

res_25_50_lOM_bfond_lFR_Lo (energeo:res_25_50_lOM_bfond_lFR_Lo)

Legend for understanding map title names and their related configuration eg: cc_15_50_lOM_bfond_hFR_Lo_geo Each parameter is separated with an underscore (_) (1) Impact category: cc= climate change, res= resources, daly= daly, eco= ecosystem (2) Life time: 15 years vs 20 years vs 25 years (3) Number of wind turbines per farm: 30 vs 40 vs 50 (max.capacity : 50 wind turbines per farm) (4) Maintenance scenario: low Operating Maintenance (lOM) or high Operating Maintenance (hOM) (5) Foundation type: only fixed (xfond) vs floating & fixed (both: bfond) (6) Failure rate: low Failure Rate (lFR) or high Failure Rate (hFR) (7) Electricity loss: 4 % electricity loss included (Lo) vs non-included electricity loss (NoLo) (8) geo: GeoTIFF file

Yearly irradiation on tilted panel derived from NASE SSE, 000 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_000_azim_Eq)

Yearly irradiation on tilted panel derived from NASE SSE, 005 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_005_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_005_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_005_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_005_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_005_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 010 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_010_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_010_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_010_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_010_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_010_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 015 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_015_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_015_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_015_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_015_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_015_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 020 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_020_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_020_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_020_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_020_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_020_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 025 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_025_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_025_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_025_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_025_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_025_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 030 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_030_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_030_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_030_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_030_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_030_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 035 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_035_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_035_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_035_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_035_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_035_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 040 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_040_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_040_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_040_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_040_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_040_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 045 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_045_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_045_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_045_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_045_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_045_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 050 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_050_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_050_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_050_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_050_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_050_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 055 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_055_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_055_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_055_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_055_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_055_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 060 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_060_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_060_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_060_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_060_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_060_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 065 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_065_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_065_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_065_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_065_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_065_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 070 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_070_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_070_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_070_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_070_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_070_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 075 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_075_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_075_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_075_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_075_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_075_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 080 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_080_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_080_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_080_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_080_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_080_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 085 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_085_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_085_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_085_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_085_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_085_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, 090 tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_090_azim_Eq)

yearly_nasa_sse_derived_irradiation_tilt_090_azim_Eq045E (project_iea:yearly_nasa_sse_derived_irradiation_tilt_090_azim_Eq045E)

yearly_nasa_sse_derived_irradiation_tilt_090_azim_Eq045W (project_iea:yearly_nasa_sse_derived_irradiation_tilt_090_azim_Eq045W)

Yearly irradiation on tilted panel derived from NASE SSE, lat tilt, Eq (project_iea:yearly_nasa_sse_derived_irradiation_tilt_lat_azim_Eq)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 000 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_000_azim_Eq)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 005 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_005_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_005_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_005_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_005_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_005_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 010 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_010_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_010_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_010_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_010_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_010_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 015 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_015_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_015_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_015_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_015_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_015_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 020 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_020_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_020_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_020_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_020_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_020_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 025 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_025_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_025_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_025_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_025_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_025_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 030 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_030_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_030_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_030_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_030_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_030_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 035 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_035_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_035_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_035_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_035_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_035_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 040 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_040_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_040_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_040_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_040_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_040_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 045 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_045_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_045_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_045_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_045_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_045_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 050 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_050_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_050_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_050_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_050_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_050_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 055 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_055_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_055_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_055_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_055_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_055_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 060 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_060_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_060_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_060_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_060_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_060_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 065 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_065_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_065_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_065_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_065_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_065_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 070 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_070_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_070_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_070_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_070_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_070_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 075 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_075_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_075_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_075_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_075_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_075_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 080 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_080_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_080_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_080_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_080_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_080_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 085 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_085_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_085_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_085_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_085_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_085_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, 090 tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_090_azim_Eq)

yearly_nasa_sse_inv_irradiation_tilt_090_azim_Eq045E (project_iea:yearly_nasa_sse_inv_irradiation_tilt_090_azim_Eq045E)

yearly_nasa_sse_inv_irradiation_tilt_090_azim_Eq045W (project_iea:yearly_nasa_sse_inv_irradiation_tilt_090_azim_Eq045W)

Yearly surface per irradiation on tilted panel derived from NASE SSE, lat tilt, Eq (project_iea:yearly_nasa_sse_inv_irradiation_tilt_lat_azim_Eq)

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