CAMS radiation service for solar energy: Exploring the error space with data-driven and spatially resolved methods and service evolution

Kurzfassung

The Copernicus Atmosphere Monitoring Service (CAMS) provides open data access to solar irradiances through its CAMS Radiation Service (CRS). Data is accessible via both the CAMS Atmospheric Data Store and user-specific Python libraries such as PV-LIB. The services meet the needs of European and national policy development and the requirements of partly commercial downstream services in the solar energy sector for e.g. planning, monitoring, efficiency improvements, and integration of renewable energies into the energy supply system. Observations from the meteorological satellites are combined with modelled aerosols, water vapor, and ozone from the CAMS Integrated Forecasting System to retrieve Surface Solar Irradiance (SSI). SSI time series in 1 min, 15 min, hourly and daily temporal resolution are produced 'on-the-fly' on user request at the desired location inside the domain of the MSG and the HIMAWARI satellites field of view. In addition to the standard access as time series, a gridded dataset in 0.1° and 15 min for the years 2004-2023 is available for land surfaces of Europe and Africa. Validation of the CRS is done regularly on a quarter-annual basis with a 6 months delay between data acquisition and assessment due to the delay in ground observation data provision from ground networks. The focus of this validation is on direct, diffuse and global irradiation components against ground observations and it provides classical, station-wise metrics such as bias, RMSE, and correlations. To widen the coverage of the quality assessment of the CRS, the ground observations database is continuously being extended with additional networks. Nevertheless, special care must be taken as these observations may have uncertainties of the order of the observed uncertainty in the CRS. Typically, in an operational setup, from the physical point of view, the retrieval of SSI is limited in the treatment of heterogeneous or multi-layer clouds and not well-defined aerosol conditions. Moreover, in a general setting the errors at the pixel-level are hard to estimate. Europe’s CAMEO project supports the CRS service evolution through a detailed assessment of the error patterns. In this project, data-driven methods e.g., SHAP analysis, are being exploited using all input parameters to bring light into the error space of the CRS. In addition, sensitivity studies and ML-based surrogate models provide additional insights on the errors. Mainly, we use retrieval inherent information to provide a single retrieval uncertainty estimate or a physics-informed bias correction. We aim at methods without the need for any auxiliary information which is not available in a stand-alone real-time satellite-based retrieval process. Furthermore, the CRS team is currently evaluating the 0.5 km visible channel of HIMAWARI to prepare for the upcoming MTG capabilities. Impact of this high-resolution information for solar radiation accuracy will be addressed, either as input for an ML-based uncertainty quantification or alternatively for a direct high-resolution retrieval product.