CAMS radiation service news – implementation of uncertainty information to the irradiance estimates

The Copernicus Atmosphere Monitoring Service (CAMS) operated by the European Center for Medium Range Forecast (ECMWF) provides open-data access to surface solar irradiance (SSI) through its CAMS Radiation Service (CRS). In CRS, observations from geostationary meteorological satellites are combined with modeled data for aerosols, water vapor, and ozone from the ECMWF CAMS Integrated Forecasting System (IFS) to derive irradiance at the Earth's surface. CRS data is operationally provided for the Meteosat satellite field of view (Europe and Africa) and the HIMAWARI satellite field of view (Asia and Australia). Time series in 1 min, 15 min, hourly and daily temporal resolution are produced 'on-the-fly' on user request at the desired location. In the expert mode, all input parameters e.g. aerosols, total column water vapour and ozone, cloud optical properties, and surface albedo can also be accessed as additional time series.

 

Recently a probabilistic error model was developed by the CAMS team (Lezaca et. al, 2025), which is designed to provide uncertainty information on the CRS irradiance estimates. For this model, an extensive database of quality controlled irradiance ground observations was exploited to condition the cumulative distribution function on the CRS estimates deviations (CRS estimates - observations) of selected CRS model inputs. By applying this uncertainty model on CRS user requests for solar irradiance time series, a complete error probability distribution can be delivered on every location and time step requested.

 

We have started to explore ways in which this uncertainty information should be presented to the CRS users for a straight forward/efficient implementation into typical processes and applications. In this work, some propositions for the metrics to be given alongside the CRS irradiance estimates will be shown, with the objective of an extensive and critical scientific exchange with CRS users, in order to fulfill as much as possible the solar energy community needs.  We will also show typical applications in which the value of this new uncertainty information is demonstrated and quantified. We investigate the use in typical energy system applications (e.g. PV plant production and feed in, energy dispatch, or instrument calibration) and the interaction or implementation with well established commercial PV modeling software (e.g., PVsyst, PVSol, or PVlib) will be tested.