Analysed databases and integrated systems

Each of the databases analysed here is integrated within a system (software setup) that provides additional tools for search, query, maps display, and calculation of derived parameters. PVGIS (the European section), includes solar radiation database developed by combination of solar radiation model and interpolated ground observations. The datasets Satel-Light and HelioClim-2 (accessible through the SoDa web portal) are built from Meteosat and MSG satellite images, respectively. NASA SSE release 6 (accessible also through RETScreen software) combines results from ISCCP

satellite project with NCAR reanalysis products. Primary data incorporated inMeteonorm version

6.1 and ESRA are developed by interpolation of ground observed data with support of satellite images (MSG and SRB, respectively). In Tab. 1 we summarise the main characteristics of the databases/systems including the quality indicators Root Mean Square Difference (RMSD) and Mean Bias Deviation (MBD).

While NASA, Satel-Light, PVGIS, and partially also Meteonorm systems are available for free, the ESRA, HelioClim-2 and full version of Meteonorm are to be purchased.

Table 1. Technical parameters of the solar radiation databases.

Database & availability

Data inputs

Period

Time

resolution

Spatial resolution (in study region)

RMSD/ MBD (%)

PVGIS Europe

(internet)

~560 meteo stations

1981-1990

Monthly

averages

1 km x 1 km + on — fly disaggreg. by 100 m DEM

4.7/-0.5

[6]

Meteonorm 6.1

(CDROM and internet)

Meteo stations + satellite data

1981-2000

Monthly

averages

Interpol. (on-fly)+ satellite; disaggreg. by 100 m DEM

6.2/0

[7]

ESRA

(CDROM)

~560 meteo stations + SRB satel. data

1981-1990

Monthly

averages

5 arc-minute x 5 arc-minute

~7.5/-

[8]

Satel-Light

(internet)

Meteosat 5, 6, 7

1996-2000

30-minute

4.6-6.2 km x 6.1-14.2 km

21.0/-0.6

[9]

HelioClim-2

(internet)

Meteosat 8 and 9 (MSG)

2004 — 2007

15-min

3.1-4.2 km x

4.1-9.6 km

25.3/2.2

[9]

NASA SSE 6

(internet)

GEWEX/SRB 3 + ISCCP satel. clouds + NCAR reanalysis

1983-2005

3-hourly

1 arc-degree x 1 arc-degree

8.7/0.3

[4]

Geographical extension of the spatial products differs: from global (NASA and Meteonorm) to cross-continental (HelioClim-2 covering Europe, Africa and Southwest Asia) and European (ESRA, PVGIS and Satel-Light). Here we focus on the subsection of the European continent (Fig. 1) where all the data sources overlap.

2. Method

2.1. Map comparison

Map-based comparison as performed here is a type of relative benchmarking of solar databases. It does not point to the “best” database, but it gives an indication of the user’s uncertainty at any location within the region. As the existing spatial products cover different periods of time, this comparison introduces also uncertainty resulting from the interannual variability of solar radiation. Here we perform a cross-comparison of maps of long-term average of yearly sum of global horizontal irradiation.

The maps from all data providers are harmonised and integrated into a geographical information system (GIS) with latitude/longitude spatial reference. The grid resolution of 5 arc minutes is chosen to provide a representative outlook at the regional (rather than local) differences within the continent. Choosing such a resolution, more detailed features that are present in the databases of HelioClim-2, Satel-Light, PVGIS and Meteonorm are suppressed (smoothed out), but the regional features are well pronounced. Integration of 6 data sources of yearly sums of global horizontal irradiation provides three results:

• Map of overall average gives the user an indication of spatial distribution of solar resource estimated by the simple averaging of the 6 datasets;

• Map of standard deviation provides information on magnitude of differences between the combined data sources, i. e. user’s uncertainty. If we assume that the estimates are normally distributed, from standard deviation a confidence interval can be calculated in which the value falls corresponding to a given probability. For example, a multiple of 1.95996 would give a range where the value from the average map falls with 95% probability.

• Maps of differences between individual databases and the overall average indicate deviation of the values in the particular dataset from the overall average.

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