| description abstract | This study examines 2?3-day solar irradiance forecasts with respect to their application in solar energy industries, such as yield prediction for the integration of the strongly fluctuating solar energy into the electricity grid. During cloud-free situations, which are predominant in regions and time periods focused on by the solar energy industry, aerosols are the main atmospheric parameter that determines ground-level direct and global irradiances. Therefore, for an episode of 5 months in Europe the accuracy of forecasts of the aerosol optical depth at 550 nm (AOD550) based on particle forecasts of a chemical transport model [the European Air Pollution Dispersion (EURAD) CTM] are analyzed as a first step. It is shown that these aerosol forecasts underestimate ground-based AOD550 measurements by a mean of ?0.11 (RMSE = 0.20). Using these aerosol forecasts together with other remote sensing data (ground albedo, ozone) and numerical weather prediction parameters (water vapor, clouds), a prototype for an irradiance forecasting system (Aerosol-based Forecasts of Solar Irradiance for Energy Applications, AFSOL) is set up. Based on the 5-month aerosol dataset, the results are then compared with forecasts of the ECMWF model and the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5), with Meteosat-7 satellite data, and with ground measurements. It is demonstrated that for clear-sky situations the AFSOL system significantly improves global irradiance and especially direct irradiance forecasts relative to ECMWF forecasts (bias reduction from ?26% to +11%; RMSE reduction from 31% to 19% for direct irradiance). On the other hand, the study shows that for cloudy conditions the AFSOL forecasts can lead to significantly larger forecast errors. This also justifies an increased research effort on cloud parameterization schemes, which is a topic of ongoing research. One practical solution for solar energy power plant operators in the meanwhile is to combine the different irradiance models depending on the forecast cloud cover, which leads to significant reductions in bias for the overall period. | |
