Validation of a Meteosat Second Generation solar radiation dataset over the northeastern Iberian Peninsula
- 1Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Dr. Fairbanks, Alaska, 99775-7320, USA
- 2Hydrology and Remote Sensing Laboratory, United States Department of Agriculture, Agriculture Research Service, BARC-West, Beltsville, Maryland, 20705 , USA
Abstract. Solar radiation plays a key role in the Earth's energy balance and is used as an essential input data in radiation-based evapotranspiration (ET) models. Accurate gridded solar radiation data at high spatial and temporal resolution are needed to retrieve ET over large domains. In this work we present an evaluation at hourly, daily and monthly time steps and regional scale (Catalonia, NE Iberian Peninsula) of a satellite-based solar radiation product developed by the Land Surface Analysis Satellite Application Facility (LSA SAF) using data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Product performance and accuracy were evaluated for datasets segmented into two terrain classes (flat and hilly areas) and two atmospheric conditions (clear and cloudy sky), as well as for the full dataset as a whole. Evaluation against measurements made with ground-based pyranometers yielded good results in flat areas with an averaged model RMSE of 65 W m−2 (19%), 34 W m−2 (9.7%) and 21 W m−2 (5.6%), for hourly, daily and monthly-averaged solar radiation and including clear and cloudy sky conditions and snow or ice cover. Hilly areas yielded intermediate results with an averaged model RMSE (root mean square error) of 89 W m−2 (27%), 48 W m−2 (14.5%) and 32 W m−2 (9.3%), for hourly, daily and monthly time steps, suggesting the need of further improvements (e.g., terrain corrections) required for retrieving localized variability in solar radiation in these areas. According to the literature, the LSA SAF solar radiation product appears to have sufficient accuracy to serve as a useful and operative input to evaporative flux retrieval models.