08 Aug 2023
 | 08 Aug 2023
Status: this preprint is open for discussion.

Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME

Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac

Abstract. With the worlwide 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, errors in NWP models can be substantial. In order to characterize in detail the performances of AROME, 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 bias is 18 W m-2 and 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 lack of consistent observations at kilometer resolution. The other situations have less impact on SWD errors. Missed cloudy situations and erroneously predicted clouds, which correspond on average 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 (198 W m-2) but results from large compensating errors. Indeed, further investigations show 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 parametrization.

Marie-Adèle Magnaldo et al.

Status: open (until 03 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1181', Anonymous Referee #1, 22 Aug 2023 reply
  • RC2: 'Comment on egusphere-2023-1181', Anonymous Referee #2, 06 Sep 2023 reply

Marie-Adèle Magnaldo et al.

Data sets

Cloud satellite products developed by the NWC Eumetsat SAF NWC Eumetsat SAF

satellite product from Copernicus Atmosphere Monitoring Service CAMS

Observations of shortwave downward radiation from the operational observation network of Météo-France Météo-France

AROME forecasts Météo-France

Model code and software

AROME Météo-France

Marie-Adèle Magnaldo et al.


Total article views: 233 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
182 42 9 233 4 4
  • HTML: 182
  • PDF: 42
  • XML: 9
  • Total: 233
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 08 Aug 2023)
Cumulative views and downloads (calculated since 08 Aug 2023)

Viewed (geographical distribution)

Total article views: 226 (including HTML, PDF, and XML) Thereof 226 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 30 Sep 2023
Short summary
With the worlwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However meteorological models that predict among others things solar radiation, have errors. Therefore, we so wanted to know in which situtaions these errors are most significant. We found that errors mostly occurs in cloudy situations, and different errors were highlighted depending of the cloud altitude. Several potential sources of errors were identified.