On statistical calibration of dual-resolution precipitation forecasts

Abstract

Recently, all leading meteorological centers release ensemble forecasts that vary in terms of ensemble size and spatial resolution, even when covering the same area. These factors significantly impact the forecast accuracy and computational resources required. In the last few years, the plans of upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9 km resolution and a 51-member ensemble with 18 km resolution induced an extensive study of the forecast skill of both raw and post-processed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) [1] ensemble model output statistic (EMOS) approach for statistical post-processing with the help of dual-resolution 24h precipitation accumulation ensemble forecasts over Europe with various forecast horizons. The high-resolution operational 50-member ECMWF ensemble is supplemented by a 200-member low-resolution (29-km grid) experimental forecast. The various dual-resolution combinations, which are equivalent in computational cost to the operational ensemble, show improved forecast skill after EMOS post-processing compared with raw ensemble combinations [3]. Additionally, the differences between these combinations are significantly reduced as a result of this post-processing technique. Moreover, the semi-locally trained CSG EMOS is fully able to catch up with the state-of-the-art quantile mapping [2] and provides an efficient alternative without requiring additional historical data essential in determining the quantile maps.