EuroSun2008-13

Point analysis

The maps give good insight into regional differences, however due to the coarse grid resolution (5 arc minutes) they smooth out local effects. Some of the integrated systems incorporate specific algorithms, such as an application of high-resolution Digital Elevation Model (DEM), which may increase the difference between results for a given site. Thus, for a set of 37 randomly selected points we analyse the values and resulting differences that a user obtains for a particular site when consulting directly each data source/system. In areas with higher agreement of all 6 databases (standard deviation lower than 4%) we selected 15 points, and in areas with higher disagreement (standard deviation between 5% and 11%) another 22 points.

For each point we calculated the yearly sum of global irradiation for (1) south-oriented surface inclined at 34 degrees (close to the typical optimum angle for PV systems in Europe), and (2) for 2-axis sun-tracking systems.

3. Results

While the map of yearly sum of global horizontal irradiation (Fig. 1) shows the average of 6 databases, the map of standard deviation (Fig. 2) indicates how much the datasets differ in various regions. Higher disagreement between the databases can be found not only in higher mountains (the Alps, Pyrenees, Carpathians, etc.), but also along some coastal zones and in flat regions, e. g. around the Baltic and North Seas. Higher standard deviation in Bulgaria and Romania, in South Scandinavia, and Po plain relates to uneven distribution of the input ground data and questionable quality of the older ground measurements that are used in ESRA and PVGIS. However, cumulative distribution of the map values (Fig. 4 right) shows that standard deviation does not exceed 6% in 78% of the study region, and only in rare (extreme) cases is it higher than 12%.

The analysis on 37 selected points shows some cases with higher differences (some of them are not present in the coarse-resolution maps) with average standard deviation 5.6%. Mean bias difference (see yellow box in the Fig. 3) indicates that satellite databases (Satel-Light and HelioClim-2) show generally higher values compared to the products relying on ground measurements (ESRA, Meteonorm and PVGIS). This difference may be partially explained by the time period of data covered. While Satel-Light and HelioClim-2 represent last 10 years, ESRA and PVGIS are based on one decade of data (1980s), and Meteonorm represents two decades (1980s and 1990s). The NASA SSE product partially spans both periods and also generally gives results that are intermediate between the two groups.

Part of the contribution from higher values obtained from HelioClim-2 database can be probably explained by Mean Bias Deviation calculated by comparison with data from 11 high-quality BSRN stations ([9] see Tab. 1).

For the same 37 points, global irradiation has been calculated for a south-oriented surface inclined at an angle of 34 degrees. Results from five systems are available (all except HelioClim-2). From linear fit of the values on the scattergram (Fig. 4 left) one can see that the standard deviation of

estimations for an inclined surface increases in average by 21% compared to the horizontal irradiation (correlation coefficient 0.98). The differences between values for 2-axis tracking surface are calculated only for 3 systems: Meteonorm, NASA SSE/RETScreen and PVGIS. Here the standard deviation increases by 36% compared to the estimates for horizontal surface (correlation coefficient 0.97). Both simulations show a good agreement in the implementation of the underlying algorithms in the compared software.

By integration of findings from the map and point analyses (Figs. 2 and 4 left) we can estimate uncertainty for any point on the map for yearly values for horizontal, inclined and 2-axis tracking surfaces. Thus the cumulative distribution of user’s uncertainty can be calculated from the map data for the whole study region. Fig. 4 (right) shows that for 90% of the study region the

uncertainty of estimates of yearly global irradiation (expressed by standard deviation) is lower than 7% for a horizontal surface, 8.3% for a south-facing surface inclined at 34°, and 10% for a 2-axis tracking surface.

4. Discussion

Differences in the estimates using several solar radiation databases relate to a number of factors.

Analysis of interannual variability for 70 meteorological stations over Europe having data for at least 10 years shows that the standard deviation from the long-term average ranges typically between 3% and 6%. To capture this variability, a database should include data for at least 5 to 10 years depending on the climate region.

There is an inherent difference between in situ (ground) and satellite observations, and the methods how these data are processed. Databases relying on the interpolation of ground observations (Meteonorm, ESRA, and PVGIS Europe) are sensitive to the quality of measurements and density of the measuring stations (which is not satisfactory in many regions), and they typically represent only statistical values. The satellite-derived databases (e. g. HelioClim, NASA SSE, and Satellight) are more affected by higher uncertainty of the cloud cover assessment when the ground is covered by snow and ice and for low sun angles, but they offer time series with high time resolution (e. g. hourly data) and provide spatially-continuous coverage.

Quality and the spatial detail of spatial database is determined by input data used in the models, mainly parameters describing the optical state of the atmosphere (such as Linke atmospheric turbidity, ozone, water vapour, aerosol optical depth), and Digital Elevation Models. The community of data developers lacks high quality aerosol data. High-resolution DEM is presently considered only in Meteonorm and PVGIS. Databases with coarser spatial resolution (e. g. NASA SSE) provide good regional estimates, however for studies at local level they may show deviations as they ignore local climate and terrain features.

The cross-comparison approach presented in this study assumes that each database has equal weight. Uncertainty can be reduced by weighting each contribution that is based on indicators of quality, number of data-years included, spatial resolution and incorporation of terrain effects. Including another two European satellite-derived databases, those owned by German Aerospace Center (DLR) and University of Oldenburg (both in Germany), and application of weighting will provide more complete picture of the uncertainty in the solar resource assessment.

5. Conclusion

This study provides a first insight into spatial distribution of uncertainty of solar radiation estimates by relative cross comparison of six data sources. In this stage only the yearly sum of global irradiation is considered, and all databases are assumed to give an equal contribution to the overall average. The map of standard deviation from the average indicates combined effect of differences between the databases, and in this study it is used as an indicator of the user’s uncertainty.

Differences at the regional level indicate that within 90% of the study region the uncertainty of yearly global irradiation estimates expressed by standard deviation does not exceed 7% for horizontal surface, 8.3% for surface inclined at 34°, and 10% for 2-axis tracking surface. A user has to expect higher differences in the outputs from the studied databases in complex climate conditions of mountains, some coastal zones and in areas where solar radiation modelling cannot rely on sufficient density and quality of input data.

References

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[2] J. Greif, K. Scharmer, Eds., European Solar Radiation Atlas (ESRA), 4th edition. Scientific advisors: R. Dogniaux, and J. Page; Authors: L. Wald, M. Albuisson, G. Czeplak, B. Bourges, R. Aguiar, H. Lund, A. Joukoff, U. Terzenbach, H.-G. Beyer, and E. P. Borisenko, Paris: Presses de l'Ecole des Mines de Paris, 2000.

[3] D. Dumortier, M. Fontoynont, D. Heinemann, A. Hammer, J. A. Olseth, A. Skartveit, P. Ineichen, C. Reise. Satel-Light, a www server which provides high quality daylight and solar radiation data for Western and Central Europe. CIE 24th Session, Warsaw, pp. 277-281, 1999. http://www. satellight. com/core. htm.

[4] Surface meteorology and Solar Energy (SSE) Release 6.0 Methodology, draft version 1.005, 2008, http://eosweb. larc. nasa. gov/sse/. Accessible also through RETScreen software http://www. retscreen. net/.

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[6] M. Shri, T. Huld, T. Cebecauer, E. D. Dunlop, Geographic Aspects of Photovoltaics in Europe: Contribution of the PVGIS Web Site. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1 (2008), in press, http://re. irc. ec. europa. eu/pvgis/.

[7] J. Remund, Quality of Meteonorm Version 6.0. Proceedings of 10th World Renewable Energy Conference, 19-25th July 1008, Glasgow. (2008).

[8] H.-G. Beyer, G. Czeplak, U. Terzenbach, L. Wald, Assessment of the method used to construct clearness index maps for the new European Solar Radiation Atlas (ESRA), Solar Energy, 61 (1997) 389-397.

[9] P. Inneichen, Irradiance products validation and comparison against 13 ground stations, Working document of the MESoR project, 2008, http://www. mesor. net/.