Statistical post-processing of ensemble forecasts at the Belgian met service

Statistical post-processing of ensemble weather forecasts has become an essential step in the forecasting chain as it enables the correction of biases and reliable uncertainty estimates of ensembles (Gneiting, 2014).  One algorithm recently proposed to perform the correction of ensemble weather forecasts is a linear member-by-member (MBM) Model Output Statistics (MOS) system, post-processing each member of the ECMWF ensemble (Van Schaeybroeck & Vannitsem, 2015). This method consists in correcting the mean and variability of the ensemble members in line with the observed climatology. At the same time, it calibrates the ensemble spread such as to match, on average, the mean square error of the ensemble mean. The MBM method calibrates the ensemble forecasts based on the station observations by minimizing the continuous ranked probability score (CRPS).

Using this method, the Royal Meteorological Institute of Belgium has started in 2020 its new postprocessing program by developing an operational application to perform the calibration of the ECMWF ensemble forecasts at the stations points for the minimum and maximum temperature, and for wind gusts. In this report, we will first describe briefly the postprocessing methods being used and the architecture of the application. We will then present the results over the first few months of operation. Finally, we will discuss the future developments of this application and of the program.

Gneiting, T., 2014: Calibration of medium-range weather forecasts. ECMWF Technical Memorandum No. 719

Van Schaeybroeck, B. & Vannitsem, S., 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141, 807–818.