Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

摘要: Solar radiation is a key factor in the Earth’s energy balance and it used as crucial input parameter many disciplines such ecology, agriculture, solar hydrology. Thus, accurate information of global downward surface shortwave flux integration into grid significant importance. From different strategies for estimates, satellite-derived numerical weather prediction forecasts are two interesting alternatives. In current work, we present comprehensive evaluation provided by Regional Atmospheric Modeling System (RAMS) Downwelling Surface Shortwave Flux (DSSF) product, derived from Meteosat Second Generation (MSG) Spinning Enhanced Visible Infrared Imager (SEVIRI). Both estimates compared to thirteen ground-based station measurements winter 2010–2011 summer 2011 seasons. For these periods, most recent versions RAMS (4.4 6.0) were running parallel within real-time forecasting system implemented over Valencia Region. The performance accuracy evaluated datasets segmented atmospheric conditions (clear cloudy skies) terrain classes (flat hilly). DSSF shows very good agreement study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging −30 40 W·m−2, root mean square (RMSE) 25 60 relative −11% 7% RMSE 22%, depending on sky condition location well, thus reproducing observations more faithfully than RAMS, which produces higher comparison measurements. this regard, statistical values varying −50 160 240 −30% 40% 10% 80%, also initialization forecast horizon. This variability demonstrates there trend deviation model results relation observations, both product forecasts, considering seasons independently. although an overestimation observed months, magnitude underestimated during winter. Finally, comparing classes, best found under clear skies flat terrain. result achieved using methodologies.