Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts

Abstract

Abstract Extended logistic regression is used to calibrate areal precipitation forecasts over two small catchments in Belgium computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) between 2006 and 2010. The parameters of the postprocessing are estimated from the hindcast database, characterized by a much lower number of members (5) than the EPS (51). Therefore, the parameters have to be corrected for predictor uncertainties. They have been fitted on the 51-member EPS ensembles, on 5-member subensembles drawn from the same EPS, and on the 5-member hindcasts. For small ensembles, a simple “regression calibration” method by which the uncertain predictors are corrected has been applied. The different parameter sets have been compared, and the corresponding extended logistic regressions have been applied to the 51-member EPS. The forecast probabilities have then been validated using rain gauge data and compared with the raw EPS. In addition, the calibrated distributions are also used to modify the ensembles of precipitation traces. The postprocessing with the extended logistic regression is shown to improve the continuous ranked probability skill score relative to the raw ensemble, and the regression calibration to remove a large portion of the bias in parameter estimation with small ensembles. With a training phase limited to a 5-week moving window, the benefit lasts for the first 2 forecast days in winter and the first 5 or 6 days in summer. In general, substantial improvements of the mean error and of the continuous ranked probability score have been shown.