Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME - M.-A. Magnaldo; Q. Libois; S. Riette; C. Lac (Geoscientific Model Development, 2024)
With the worldwide development of the solar energy sector, the need for reliable surface shortwave downward radiation (SWD) forecasts has significantly increased in recent years. SWD forecasts of a few hours to a few days based on numerical weather prediction (NWP) models are essential to facilitate the incorporation of solar energy into the electric grid and ensure network stability. However, SWD errors in NWP models can be substantial. In order to characterize the performances of AROME in detail, the operational NWP model of the French weather service Météo-France, a full year of hourly AROME forecasts is compared to corresponding in situ SWD measurements from 168 high-quality pyranometers covering France. In addition, to classify cloud scenes at high temporal frequency and over the whole territory, cloud products derived from the Satellite Application Facility for Nowcasting and Very Short Range Forecasting (SAF NWC) from geostationary satellites are also used. The 2020 mean bias is positive, with a value of 18 W m−2, meaning that AROME on average overestimates the SWD. The root-mean-square error is 98 W m−2. The situations that contribute the most to the bias correspond to cloudy skies in the model and in the observations, situations that are very frequent (66 %) and characterized by an annual bias of 24 W m−2. Part of this positive bias probably comes from an underestimation of cloud fraction in AROME, although this is not fully addressed in this study due to the lack of consistent observations at kilometer resolution. The other situations have less impact on SWD errors. Missed cloudy situations and erroneously predicted clouds, which generally correspond to clouds with a low impact on the SWD, also have low occurrence (4 % and 11 %). Likewise, well-predicted clear-sky conditions are characterized by a low bias (3 W m−2). When limited to overcast situations in the model, the bias in cloudy skies is small (1 W m−2) but results from large compensating errors. Indeed, further investigation shows that high clouds are systematically associated with a SWD positive bias, while low clouds are associated with a negative bias. This detailed analysis shows that the errors result from a combination of incorrect cloud optical properties and cloud fraction errors, highlighting the need for a more detailed evaluation of cloud properties. This study also provides valuable insights into the potential improvement of AROME physical parametrizations.