The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China

Evaluating the adaptability of precipitation forecasting is of great importance for regional flood control and drought warnings. This study conducted evaluations using the 1–9 days cumulative precipitation forecast data of five typical operational global ensemble prediction systems (EPSs) from TIGGE (i.e., The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble) and the observed daily precipitation data of 40 meteorological stations over the Huaihe River basin (HB). A series of verification metrics is used to evaluate the performances of quantitative precipitation forecasts (QPFs) and probabilistic quantitative precipitation forecasts (PQPFs) from the five EPSs from April to December 2015 in terms of overall performance, different precipitation thresholds, different lead times and the spatial distribution over the HB. The adaptability of the multimodel superensemble integrated from the five EPSs by the Bayesian model average is also examined during the main flood season. The results show that (1) the forecast quality of the China Meteorological Administration EPS is the worst for all lead times, which may relate to its having the fewest ensemble members. The European Centre for Medium-Range Weather Forecasts (ECMWF) EPS performs the best in terms of QPF and PQPF qualities for longer lead times because ECMWF has the largest ensemble members. (2) All EPSs have better discrimination at low thresholds, indicating the reference value for drought warnings. ECMWF is expected to obtain the best PQPF skill for a large threshold through postprocessing; (3) due to the differences in climates in the North and South of the basin, QPF and PQPF qualities are better in the northern HB than in the southern HB; (4) except for climate, the PQPF skill is also influenced by precipitation type, while the QPF accuracy is affected by terrain. The PQPF is good at forecasting the precipitation caused by ocean effects but not by mountain topography. The QPF accuracy decreases in mountainous areas; and (5) the multimodel superensemble has little effect on PQPF skill improvement but can improve QPF accuracy when raw EPSs have significantly different QPF accuracies.