SolarGIS Data Specification

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SolarGIS version 1.8: Specifications of solar radiation and meteo databasehttp://solargis.info

2012 GeoModel Solar 1

SolarGIS Database version 1.8satellite-derived solar radiation and meteorological data

OVERVIEW

GeoModel Solar operates high-resolution meteorological database - SolarGIS. The database consists ofthe following primary parameters:

Solar radiation: Global horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI)

Meteo: Air Temperature at 2 metres (TEMP), Relative Humidity (RH), Wind Speed (WS) and WindDirection (WD) at 10 metres. Rainfall data are in preparation.

Meteorological data is available globally. Solar radiation data is available for more regionss of Europe,Africa, Asia, West Australia, and North and South America. Complete coverage of solar radiation data isexpected by mid-2013.

SOLAR RADIATION

Solar radiation primary parameters are derived by advanced and scientifically validated models, which usesatellite data from the Meteosat ( EUMETSAT, DE) and GOES ( NOAA, USA) mission, and outputs fromthe MACC and GFS atmospheric models ( ECMWF, UK and NOAA NCEP, USA). An independentExpert Survey (International Energy Agency, SHC Task 36 Data cross-comparison1) has identified theSolarGIS as the best database in the market, in terms of accuracy, reliability and data representativeness.

Solar database - input data

Cloud Index and Snow Index calculated from Meteosat and GOES satellites ( EUMETSAT, NOAA) Water Vapour derived from GFS database ( NOAA NCEP) Atmospheric Optical Depth calculated from MACC database ( ECMWF) Snow Depth from GFS and CSFR ( NOAA) Elevation and horizon profile calculated from Digital Elevation Model SRTM-3 ( SRTM team)

Calculation method

Solar radiation is calculated by numerical models, which are parameterized by a set of inputs characterizingthe cloud transmittance, state of the atmosphere and terrain conditions. The methodology is described inseveral papers [1, 2, 3].

In the SolarGIS approach, the clear-sky irradianceis calculated by the simplified SOLIS model, developedby Ineichen [4]. This model allows fast calculation of clear-sky irradiance from the set of input parameters.Sun position is a deterministic parameter, and is described by the numerical models with satisfactoryaccuracy. Stochastic variability of clear-sky atmospheric conditions is determined by changingconcentrations of atmospheric constituents, namely aerosols, water vapour and ozone. The calculationaccuracy of the clear-sky irradiance, especially DNI, is sensitive to the information about aerosols.

The key factor determining short-term variability of all-sky irradiance is clouds. Attenuation effect ofcloudsis expressed by the means of a parameter called cloud index, which is calculated from the routineobservations of meteorological geostationary satellites. Spatial resolution of satellite data used in SolarGIS

1seehttp://solargis.info/doc/index.php?select=74#20110225
http://solargis.info/doc/index.php?select=74#20110225http://solargis.info/doc/index.php?select=74#20110225http://solargis.info/doc/index.php?select=74#20110225http://solargis.info/doc/index.php?select=74#20110225


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is about 4 x 5 km at mid latitudes (3 km at sub-satellite point) and the time step is 15 and 30 minutes. Toretrieve all-sky irradiance in each time step, the clear-sky global horizontal irradiance is coupled with cloud

index. Effect of clouds in SolarGIS is calculated from the Meteosat and GOES satellite data in the form ofcloud index (cloud transmittance). The SolarGIS algorithms are based on the Heliosat-2 calculation schemeand the Perez approach [5], which has been updated and supplemented by multispectral data processing.The cloud index is derived by relating irradiance recorded by the satellite in several spectral channels andsurface albedo to the cloud optical properties. A number of improvements have been introduced to bettercope with specific situations such as snow, ice, or high albedo areas (arid zones and deserts), and alsowith complex terrain. The improved snow detection algorithms are based on work of Romanov and Tarpley[6],and Duerr and Zelenka [7].

In the SolarGIS approach, the new generation aerosol data set representing Atmospheric Optical Depth(AOD) is used. This data set has been developed and are operationally calculated by MACC project (ECMWF) [8]. Important feature of this AOD data set is that it captures daily variability of aerosols andallows simulating more precisely the events with extreme atmospheric load of aerosol particles. Thus it

reduces uncertainty of instantaneous estimates of GHI and especially DNI and allows for improveddistribution of irradiance values. It is to be noted that coverage of high frequency (daily) aerosol data islimited to the period from 2003 onwards; the remaining years (1994 to 2002) are represented only bymonthly long-term averages. Adaptation of MACC daily Atmospheric Optical Depth data in SolarGIS hasbeen described in publications by Cebecauer and Suri [3, 9]. AOD data are routinely calculated at a spatialresolution of about 125 km.

Water vapour is also highly variable in space and time, but it has lower impact on the values of solarradiation, compared to aerosols. The daily GFS and CFSR values ( NOAA NCEP) are used in SolarGIS,thus representing the daily variability from 1994 to the present.

Ozoneabsorbs solar radiation at wavelengths shorter than 0.3 m, thus having negligible influence on thebroadband solar radiation.

Direct Normal Irradiance (DNI) is calculated from Global Horizontal Irradiance (GHI) using modifiedDirindex model [10]. Diffuse irradiance for tilted surfaces is calculated by Perez model [11]. Terrainenhancement procedure is based on the disaggregation algorithms of Ruiz Arias et al. [ 12].

SolarGIS solar radiation model offers a number of unique innovations, which together withimplementation of new atmospheric datasets position SolarGIS solar resource database as unique on themarket. The key innovations include:

Algorithms for maintaining high stable positional and radiometric accuracy of input satellite data,along the all time coverage (from year 1994 onwards);

Use of multispectral channels and multidimensional statistical treatment of ground albedo, whichincrease accuracy in arid and coastal zones, and in regions with snow cover and ice;

Use of daily values of aerosol and water vapour data, which offers data with higher accuracy (lowerbias) and better statistical distribution of irradiances (improved histogram and reduced RMSD).

New algorithms have been implemented for enhanced spatial resolution by high-resolution DigitalTerrain Model of up to 100 metres;

New computer architecture and data storage models make it possible to deliver a number of solarresource and meteo parameters in real-time online, and all high-value data products within 1-2working days.

The uncertainties in the data and numerical models are described in publication [ 13].


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Output Parameters

Primary parameters: Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) 15-minute (30-minute) time series

Secondary (derived) parameters: Global in-plane irradiance for fixed and sun-tracking surfaces Diffuse irradiance 15-min (30-min) time series or aggregated values (hourly, daily, monthly averages) Typical Meteorological Year (TMY) Long-term monthly averages

Primary co-ordinate system

WGS84 is used for geographic coordinates (latitude/longitude), ellipsoid and datum. SRTM-3 DEM is usedfor altitude correction of atmospheric parameters. Data can be recalculated to any other co-ordinatesystem.

Spatial resolution

Calculation scheme is applied in Meteosat and GOES data resolution (approximately 3 km at sub-satellitepoint). Model outputs are resampled to 2 arc-minutes regular grid in WGS84 geographical coordinatesystem. The spatial resolution of the time series data products is enhanced by terrain SRTM-3 (3 arc-seconds, i.e. about 90 metres at the equator). Data can be recalculated to any other spatial resolution.

Temporal resolution

Nominal time of the data products: 15-min (30-min) instantaneous, and 60 min average values. In the 60-minute average, the time step refers to the end of hour. Data is also available as aggregated daily andmonthly statistics, including long-term averages and Typical Meteorological Years (TMYs).

Temporal coverage

From 1994 (from 1999 in Asia, Americas, and West Australia) up to the present time, i.e. 19+ (14+) years ofcontinuous data coverage. In the SolarGIS database, the input atmospheric parameters (aerosols andwater vapour) represent daily data.

Time reference

Where relevant, data are provided in UTC (upon request True Solar Time can be used).

Data gaps

More than 99% coverage of satellite data for Meteosat regions. More gaps occur in the GOES East data forSouthern hemisphere. Data for very low sun angles are derived by extrapolation of clear-sky index. Thesupplied data have all the gaps filled using the intelligent algorithms. Flagging:

0:sun below horizon1: model value2: interpolated


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4: interpolated/extrapolated >1hour5: long term monthly median

Data coverage

Solar data is available in a homogeneous spatial and temporal coverage as can be seen on the map below.The data coverage is being expanded in steps, and it is expected to reach global coverage by mid-2013.Part of the database can be accessible also online via applications: http://solargis.info/imaps/ andhttp://solargis.info/pvplanner/

Data available: Online On Request

Quality

Quality is compared with high-quality ground measurements from worldwide meteo stations. The qualitystandards, as defined by the International Energy Agency SHC Task 36 consortium, are used.

The relative mean bias (rMB) in Europe, for Global Horizontal Irradiance GHI is 1.1%, and root meansquare difference (rRMSD) is 18.5%, 9.6% and 4.8% for hourly, daily and monthly data, respectively.

Mean bias of GHI in the Alps is between -8% and +4% and hourly rRMSD is around 30%, and the datashow significant improvements in mountains, coastal zones, and arid areas.

The mean bias of Direct Normal Irradiance(DNI) in Europe is -3.1 W/m2(-0.9%), standard deviation of

biases 6.4% and Root Mean Square Deviation (RMSD) is 123, 75, and 32 W/m2(35.7%, 21.9% and 9.3%),

for hourly, daily and monthly data, respectively.

In Spain, the DNI model was validated against data from 14 ground stations. The mean bias of DNI is16.4 W/m

2(4.2%), standard deviation of biases 3.9%, and hourly RMSD is 110 W/m

2(32.2%). For sites in

desert (Tamanrasset and SedeBoqer) the quality validation shows higher deviation: mean bias is -26 and -71 W/m

2(-4.2 and -11.7%), respectively, and RMSD is 144 and 174 W/m

2(23.3 and 28.4%) respectively.

Quality assessment of DNI on 6 sites in South Africashows very low bias - within the range of 2.5%, andhourly RMSE - in the range of 16% to 22%.
http://solargis.info/imaps/http://solargis.info/imaps/http://solargis.info/pvplanner/http://solargis.info/pvplanner/http://solargis.info/pvplanner/http://solargis.info/imaps/


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More validation please find below.

Quality flags or measures of uncertainty

Gap filling flag is part of data delivery. Quantitative estimate of the uncertainty of values are available on thedemand.

METEOROLOGICAL DATA

The SolarGIS database also includes air temperatureat 2 metresdata, which are calculated from NOAANCEP data sources, and validated by ground measurements. Air temperature is available for the periodfrom January 1994 to the present time. The data are disaggregated by SolarGIS method to reflect variabilityinduced by high-resolution terrain. Spatial resolution of the final output database is 1 km.

As an optional supplement, wind speed and wind direction at 10 metres, and relative humidity can beincluded as ancillary parameters to the standard data parameters.

Important note:Meteo parameters derived from the numerical weather model outputs (air temperature,

relative humidity, wind speed and wind direction) have lower spatial and temporal resolution; they may notrepresent the site specific-conditions, as the solar resource data. Especially wind and relative humidity datahave higher uncertainty, and they provide only information with regional validity, which can be used only asancillary information for solar energy projects.

Meteorological parameters have global coverage.

DATA FORMATS AND DELIVERY OPTIONS

Time series typically in ASCII CSV, SAM, PVSYST and other data formats. Smaller averaged data could bedelivered also in xls. Metadata and file structure is described in the file header. Spatial data could bedelivered in ESRI ASCII GRID format, GeoTIFF, Google Earth KML/KMZ data formats or NetCDF.

Data transfer options:

Email, ftp, http-download, web services

HOW SOLARGIS SOLAR DATA COMPARE WITH OTHER DATABASES

The recent IEA Task 36 data inter-comparison activity, lead by Pierre Ineichen from University of Geneva,has independently confirmed that SolarGIS is the best performing solar radiation database presentlyavailable on the market. More information at:

http://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-annexe.pdf

An example from the report is given below - the yellow points represent the measured data and the bluepoints represent satellite-derived data. It can be seen that some satellite models are not capable torepresent all the measured values, for example higher irradiance values, and therefore they are not able toprovide coherent information needed for system design, financing and monitoring.
http://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-annexe.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-annexe.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-annexe.pdfhttp://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report.pdf


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Quality assurance procedures applied

Internal check and approval of data is applied in several steps:

- Site identification (coordinates and altitude). User is requested to confirm the site location by visualcheck of coordinates using SolarGIS iMaps application

- On the request, surrounding terrain shading effect analysis is integrated in data calculation

- Cross comparison with other data is performed. Visual check of daily profiles to identify outliers

SolarGIS


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COPYRIGHT AND TERMS OF USE

SolarGIS 2013 GeoModel Solar s.r.o. License to the use of SolarGIS data will be granted, however noredistribution of purchased SolarGIS data is permitted without prior written permission from GeoModelSolar. SolarGISis the registered trademark of GeoModel Solar.

Terms of use - read more in the Data License Agreement:

http://solargis.info/doc/_docs/SolarGIS_Data_License_Agreement.pdf

DOCUMENTATION

[1] Cebecauer T., ri M., Perez R., High performance MSG satellite model for operational solar energy applications.ASES National Solar Conference, Phoenix, USA, 2010.

[2] ri M., Cebecauer T., Perez P., Quality procedures of SolarGIS for provision site-specific solar resourceinformation. Conference SolarPACES 2010, September 2010, Perpignan, France.

[3] Cebecauer T., ri M., Accuracy improvements of satellite-derived solar resource based on GEMS re-analysisaerosols. Conference SolarPACES 2010, September 2010, Perpignan, France.

[4] Ineichen P., A broadband simplified version of the Solis clear sky model. Solar Energy, 82, 8, 758-762, 2008.

[5] Perez R., Ineichen P., Moore K., Kmiecik M., Chain C., George R., Vignola F., A New Operational Satellite-to-Irradiance Model. Solar Energy 73, 307-317, 2002.

[6] Romanov P., Tarpley D., Enhanced algorithm for estimating snow depth from geostationary satellites, RemoteSensing of Environment, 108, 97-110, 2007.

[7] Drr B., Zelenka A, Deriving surface global irradiance over the Alpine region from METEOSAT Second Generati ondata by supplementing the HELIOSAT method, International Journal of Remote Sensing, 30, 22, 5821-5841, 2009.

[8] Morcrette J., Boucher O., Jones L., Salmond D., Bechtold P., Beljaars A., Benedetti A., Bonet A., Kaiser J.W.,Razinger M., Schulz M., Serrar S., Simmons A.J., Sofiev M., Suttie M., Tompkins A., Uncht A., GEMS-AER team.Aerosol analysis and forecast in the ECMWF Integrated Forecast System. Part I: Forward modelling. Journal ofGeophysical Research, 114, 2009.

[9] Cebecauer T., MACC aerosols in solar radiation modelling for energy applications. Presentation. MACCConference on Monitoring and Forecasting Atmospheric Composition, May 2011, Utrecht, Netherlands, 2011.

[10] Perez R., Ineichen P., Maxwell E., Seals R. and Zelenka A., Dynamic global-to-direct irradiance conversionmodels. ASHRAE Transactions-Research Series, pp. 354-369, 1992.

[11] Perez, R., Seals R., Ineichen P., Stewart R., Menicucci D.. A new simplified version of the Perez diffuse irradiance

model for tilted surfaces. Solar Energy, 39, 221-232, 1987.[12] Ruiz-Arias J. A., Cebecauer T., Tovar-Pescador J., ri M., 2010. Spatial disaggregation of satellite-derived

irradiance using a high-resolution digital elevation model. Solar Energy, 84, 1644-1657.

[13] Cebecauer T., ri M., Guyemard C. A., 2011. Uncertainty Sources in Satellite-Derived Direct Normal Irradiance:How Can Prediction Accuracy Be Improved Globally?. Proceedings of the SolarPACES Conference, September2011, Granada, Spain.

See more at:http://geomodelsolar.eu/publications
http://solargis.info/doc/_docs/SolarGIS_Data_License_Agreement.pdfhttp://solargis.info/doc/_docs/SolarGIS_Data_License_Agreement.pdfhttp://geomodelsolar.eu/publicationshttp://geomodelsolar.eu/publicationshttp://geomodelsolar.eu/publicationshttp://geomodelsolar.eu/publicationshttp://solargis.info/doc/_docs/SolarGIS_Data_License_Agreement.pdf


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Validation sites for DNI

Site Country Latit. Longit. Elev. MBD rMBDrRMSDhourly

rRMSDdaily

rRMSDmonthly

[deg] [deg] [m][W/m

2][%] [%] [%] [%]

Tartu Toravere Estonia 58.265 26.466 70 -29 -11.8 48.8 30.6 17.6

Camborne UK 50.217 -5.317 88 -1 -0.6 45.3 23.6 5.5

Kishinev Moldova 47.001 28.816 205 -23 -7.6 34.4 20.9 12.6

HradecKralove

CzechRepublic

50.183 15.833 236 -27 -11.5 44.6 27.3 16.7

Hamburg Germany 53.633 10.000 14 -31 -9.3 32.3 22.8 14.1Weihenstephan

Germany 48.400 11.700 472 -15 -4.3 38.0 23.1 9.3

Freiburg Germany 47.979 7.831 275 -9 -3.3 36.8 16.7 6.7

Payerne Switzerland 46.814 6.943 490 -10 -2.5 32.7 21.6 11.1

Davos Switzerland 46.813 9.845 1610 21 7.2 58.7 27.0 10.2

Geneve Switzerland 46.200 6.132 420 0 0.2 39.3 22.2 8.4

Locarno-Monti Switzerland 46.173 8.787 370 -14 -4.5 48.9 30.4 6.9

Vaulx un Velin France 45.779 4.923 170 -12 -4.1 35.0 20.7 10.6

Carpentras France 44.083 5.059 99 -1.1 23.8

A Coruna Spain 43.367 -8.419 58 -8 -2.8 47.0 33.4 12.3

Oviedo Spain 43.354 -5.873 336 8 3.1 50.2 27.0 8.5

San Sebastian Spain 43.308 -2.039 252 4 1.5 37.8 19.0 5.0Soria Spain 41.767 -2.467 1082 3 0.9 34.1 18.8 4.9

Valladolid Spain 41.650 -4.767 735 18 4.3 28.1 16.3 7.7

Lleida Spain 41.626 0.595 192 -1 -0.2 33.3 20.6 8.8

Madrid Spain 40.453 -3.724 664 -19 -4.1 25.2 15.7 5.4

Palma Spain 39.567 2.744 4 6 1.6 19.9

Caceres Spain 39.472 -6.339 405 8 1.8 30.6 18.1 6.7

Badajoz Spain 38.886 -7.012 175 28 6.3 27.8 18.2 7.2

PSA Tabernas Spain 37.093 -2.357 498 -2.4 23.8

Murcia Spain 38.003 -1.169 62 4 0.9 25.6 16.1 6.0

Cordoba Spain 37.844 -4.851 91 33 8.8 33.7 21.7 11.6

Malaga Spain 36.719 -4.480 60 43 11.0 32.4 24.6 12.9

Izana Spain 28.309 -16.499 2371 -15 -2.2 30.2 21.8 7.2

San Bartolome Spain 27.758 -15.576 50 4 0.9 28.6 17.4 2.6

Sede Boqer Israel 30.855 34.782 480 -4.8 23.7

Solar Village Saudi Arabia 24.910 46.410 757 -5.1 26.0

Xianghe China 39.754 116.962 32 3 1.3 50.2 37.1 11.4

Tamanrasset Algeria 22.783 5.514 1378 1.4 21.2

Sonbesie South Africa -33.928 18.865 120 -34 -6.4 20.1 12.1 7.8

De Aar South Africa -30.667 24.000 1331 -6 -1.0 16.8 9.9 2.4

Aggeneys South Africa -29.295 18.805 789 -25 -3.7 18.3 11.5 5.2

Paulputs South Africa -28.880 19.565 823 -54 -7.8 18.0 12.4 9.3

Upington South Africa -28.468 21.072 864 -41 -6.1 19.8 12.5 8.2


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Durban South Africa -29.900 30.980 151 -22 -5.8 32.2 20.3 8.0

Petrolina Brazil -9.068 -40.319 387 1.7 22.8 8.7 2.3

Brasilia Brazil -15.601 -47.713 1023 8.2 38.3 21.4 9.9

Florianopolis Brazil -27.533 -48.517 11 -6.1 36.8 21.1 7.2So Martinhoda Serra Brazil -29.443 -53.823 489 -3.8 25.0 12.9 5.2

Broome Australia 122.235 -17.947 7 -0.1 24.9 11.6 4.5

Learmonth Australia 114.097 -22.241 5 -5.2 19.2 9.8 5.6

Geraldton Australia 114.698 -28.795 33 -0.7 24.5 12 3Kalgoorie-Boulder Australia 121.453 -30.785 365 0.8 24.9 10.7 3Cocos(Keeling)Islands

Cocosislands 96.834 -12.189 3 -4.5 35.9 18.7 7.2

MBD: Mean Bias DeviationrMBD: relative Mean Bias DeviationrRMSD: relative Root Mean Square Deviation

Validation sites for DNI


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Validation sites for GHI

Site Country Lat. Longit. Elev. MBD rMBD rRMSD rRMSD rRMSD

[deg] [deg] [m] [W/m2] [%] [%] [%] [%]

Bergen Norway 60.384 5.332 45 14 8.2 32.2 17.0 11.0

Tartu Toravere Estonia 58.265 26.466 70 -4 -1.8 23.3 10.8 3.7

Zoseni Latvia 57.133 25.917 188 -14 -5.1 23.4 13.3 9.4

LiepajaRucava

Latvia 56.483 21.017 4 -6 -2.2 17.8 8.7 3.3

Wroclaw Poland 51.126 17.014 111 7.6 3.2 19.5 9.0 4.1

Lerwick UK 60.133 -1.183 84 3 1.5 28.5 14.1 3.3

Loughborough UK 52.770 -1.230 70 -4 -1.7 24.4 11.8 4.2

Camborne UK 50.217 -5.317 88 -4 -1.6 19.9 8.4 2.9

Kishinev Moldova 47.001 28.816 205 3 0.9 16.7 7.5 1.9

HradecKralove

Czech R. 50.183 15.833 2363.6 1.5 22.2 10.0 3.2

Luka Czech R. 49.650 16.950 510 0.1 0.0 22.1 10.8 5.0

Doksany Czech R. 50.783 14.283 185 1.4 0.5 18.5 9.0 4.5

Kocelovice Czech R. 49.466 13.833 519 -5.5 -2.0 18.9 8.9 4.8

Schleswig Germany 54.518 9.570 12 -11 -4.5 22.0 12.5 7.6

Hamburg Germany 53.633 10.000 14 3 1.6 20.8 9.5 3.3

Potsdam Germany 52.367 13.083 107 -7 -2.7 18.2 8.2 4.0

Weihenstepha

nGermany 48.400 11.700 472 -6 -2.3 20.4 10.1 3.7

Freiburg Germany 47.979 7.831 275 12 3.9 19.4 8.3 4.4

Weissfluhjoch Germany 46.833 9.805 2690 -9 -2.8 31.5 18.0 8.9

SLFVersuchsf.

Germany 46.828 9.809 2540 -7 -2.4 32.2 18.0 9.0

Mannlichen Germany 46.613 7.941 2230 -5 -1.7 30.1 15.9 6.4

Jungfraujoch Germany 46.549 7.985 3580 -5 -1.3 32.7 20.8 11.5

Wien Austria 48.249 16.356 203 6.7 2.5 20.6 9.3 3.1

Bratislava Slovakia 48.170 17.072 195 7.5 3.0 19.2 9.7 4.2

Hurbanovo Slovakia 47.873 18.190 113 -1 -0.2 20.9 10.8 4.8

Ganovce Slovakia 49.033 20.317 706 -4.2 -1.6 26.3 12.5 3.2

Payerne Switzerland 46.814 6.943 490 -1 -0.2 14.8 7.8 1.8

Davos Switzerland 46.813 9.845 1610 -12 -3.7 27.5 14.0 5.4Eggishorn Switzerland 46.427 8.093 2895 5 1.6 42.1 27.0 15.3

Cimetta Switzerland 46.201 8.790 1670 18 6.1 27.3 14.6 8.0

Geneve Switzerland 46.200 6.132 420 11 3.9 19.3 8.9 4.0

Locarno-Monti Switzerland 46.173 8.787 370 -1 -0.3 17.7 7.9 2.1

Gornergrat Switzerland 45.984 7.785 3110 -25 -6.6 31.1 19.3 11.2

Zagreb Croatia 45.819 16.013 119 5 1.7 19.2 8.1 3.2

Ispra Italy 45.812 8.627 220 13 4.6 15.4 7.7 4.8

Gospic Croatia 44.549 15.361 565 3 1.2 24.7 11.1 3.2

Nantes France 47.254 -1.554 30 -4 -1.4 18.3 9.4 2.7

Vaulx un Velin France 45.779 4.923 170 15 5.5 17.4 8.8 5.8

Carpentras France 44.083 5.059 99 1.1 13.2


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A Coruna Spain 43.367 -8.419 58 -5 -1.7 18.0 9.2 3.5

Oviedo Spain 43.354 -5.873 336 19 7.1 24.2 13.6 7.5

San Sebastian Spain 43.308 -2.039 252 5 1.6 19.4 8.3 3.2

Soria Spain 41.767 -2.467 1082 -2 -0.7 18.2 7.2 1.5

Valladolid Spain 41.650 -4.767 735 11 3.0 14.0 6.8 3.6

Lleida Spain 41.626 0.595 192 -6 -1.5 13.2 7.4 3.9

Barcelona Spain 41.386 2.117 125 12 3.4 14.8 6.7 4.0

Madrid Spain 40.453 -3.724 664 5 1.3 13.3 6.3 1.9

Palma Spain 39.567 2.744 4 -1 -0.3 13.7 5.7 1.5

Valencia Spain 39.489 -0.471 57 26 7.1 18.4 12.5 8.2

Caceres Spain 39.472 -6.339 405 10 2.5 13.7 6.7 3.1

Badajoz Spain 38.886 -7.012 175 6 1.5 11.8 5.3 2.4

PSA Tabernas Spain 37.093 -2.357 498 0.8 13.5

Penteli Spain 38.050 23.860 430 -13 -3.0 16.6 7.5 3.2Murcia Spain 38.003 -1.169 62 3 0.8 12.2 5.7 1.7

Cordoba Spain 37.844 -4.851 91 12 2.6 11.7 6.4 4.0

Mugla Spain 37.214 28.368 662 22 5.7 20.1 10.6 5.7

Malaga Spain 36.719 -4.480 60 10 2.6 14.6 7.2 3.1

Izana Spain 28.309 -16.499 2371 -22 -4.1 16.7 10.4 4.6

San Bartolome Spain 27.758 -15.576 50 0 0.0 13.7 5.9 1.1

Thessaloniki Greece 40.632 22.959 60 2 0.4 13.5 5.7 1.7

Athens Greece 37.972 23.718 107 14 3.9 16.1 8.4 4.3

Crete Greece 35.300 25.100 122 8 2.2 13.4 6.8 2.6

Amman Jordan 32.025 35.879 1041 -10 -1.9 9.6 3.8 1.9

Sede Boqer Israel 30.855 34.782 4806 1.0 10.6 6.2 2.1Solar Village Saudi Arabia 24.910 46.410 757 -2 -0.3 9.6 5.5 2.0

Ishigakijima Japan 24.337 124.163 11 10 2.7 34.2 18.8 11.3

Pantnagar India 29.046 79.521 241 -11 -2.5 17.6 11.6 3.5

Kanpur India 26.513 80.232 123 -5 -1.2 15.5 8.0 2.2

Silpakorn Thailand 13.819 100.041 72 -18 -4.5 22.3 9.3 5.4

Xianghe China 39.754 116.962 32 12 3.6 23.4 15.9 4.6

Tamanrasset Algeria 22.783 5.514 1378 -0.9 8.6

Sonbesie South Africa -33.928 18.865 120 -9.9 -2.3 14.8 9.0 7.1

De Aar South Africa -30.667 24.000 1331 8.2 1.8 11.5 6.9 2.5

Durban South Africa -29.900 30.980 151 4.5 1.2 15.5 7.5 3.6

Agoufu Mali 15.345 -1.479 290 -1 10.9 6.1 2.9

Bamba Mali 17.099 -1.402 272 -2.2 12 7.7 5.1

Banizoumbou Niger 13.531 2.661 211 -1.8 12.3 7.5 4.8

Djougou Benin 9.692 1.662 438 2.7 16.8 9.6 5.4

M Bour Senegal 14.394 -16.959 5 1.9 11.2 6.4 3.3

Ilorin Nigeria 8.533 4.567 350 7.9 23.4 14 10.7

Petrolina Brazil -9.068 -40.319 387 2.4 13.5 6.4 2.9

Brasilia Brazil -15.601 -47.713 1023 5 23.9 10.9 6.3

Florianopolis Brazil -27.533 -48.517 11 -1.0 21.6 9.3 2.6So Martinhoda Serra Brazil -29.443 -53.823 489 -1.5 21.6 9.3 2.6

Broome Australia 122.235 -17.947 7 3 0.5 14.7 6.5 1.8

Learmonth Australia 114.097 -22.241 5 -16 -2.9 10.9 5.1 3.1


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Geraldton Australia 114.698 -28.795 33 -10 -1.9 13.6 5.7 2.4Kalgoorlie-

Boul. Australia 121.453 -30.785 365 -4 -1.0 14.4 5.3 1.2Cocos Islands Cocos islands 96.834 -12.189 3 -13 -2.8 17.0 7.4 3.8

MBD: Mean Bias DeviationrMBD: relative Mean Bias DeviationrRMSD: relative Root Mean Square Deviation

Validation sites for GHI

CONTACT

GeoModel Solar s.r.o., Pionierska 15, 831 02 Bratislava, Slovakia

Company ID: 45 354 766, VAT Number: SK2022962766

Registration: Companies Register, District Court Bratislava I, Section Sro, File 62765/B

http://solargis.info,[email protected],tel: +421 2 492 12 491, fax: +421 2 492 12 423

Last update: 18 March 2013
http://solargis.info/http://solargis.info/mailto:[email protected]:[email protected]:[email protected]:[email protected]://solargis.info/

Documents

SolarGIS Data Specification