A probabilistic forecast approach for daily precipitation totals.

Abstract Commonly, post-processing techniques are employed to calibrate a model forecast. Here, we present a probabilistic post-processor that provides calibrated probability and quantile forecasts of precipitation on the local scale. The forecasts are based on large-scale circulation patterns of the 12h forecast from the NCEP High Resolution Global Forecast System. The censored quantile regression is used to estimate selected quantiles of the precipitation amount and the probability of the occurrence of precipitation. The approach accounts for the mixed discrete-continuous character of daily precipitation totals. The forecasts are verified using a new verification score for quantile forecasts, namely the censored quantile verification (CQV) score. The forecast approach is as follows. First, a canonical correlation is employed to correct systematic deviations in the GFS large-scale patterns compared to the NCEP or ERA40 reanalysis. Secondly, the statistical quantile model between the large-scale circulation and the local precipitation quantile is derived using NCEP and ERA40 reanalysis data. Then, the statistical quantile model is applied to 12h forecasts provided by the GFS forecast system. The probabilistic forecasts are reliable and the relative gain in performance of the quantile as well as the probability forecasts compared to the climatological forecasts range between 20% and 50%. The importance of the various parts of the post-processing are assessed, and the performance is compared to forecasts based on the direct precipitation output from the ECMWF forecast system.

Quantile forecast discrimination ability and value

While probabilistic forecast verification for categorical forecasts is well established, some of the existing concepts and methods have not found their equivalent for the case of continuous variables. New tools dedicated to the assessment of forecast discrimination ability and forecast value are introduced here, based on quantile forecasts being the base product for the continuous case (hence in a nonparametric framework). The relative user characteristic (RUC) curve and the quantile value plot allow analysing the performance of a forecast for a specific user in a decision-making framework. The RUC curve is designed as a user-based discrimination tool and the quantile value plot translates forecast discrimination ability in terms of economic value. The relationship between the overall value of a quantile forecast and the respective quantile skill score is also discussed. The application of these new verification approaches and tools is illustrated based on synthetic datasets, as well as for the case of global radiation forecasts from the high resolution ensemble COSMO-DE-EPS of the German Weather Service