利用海河流域逐日降水历史观测资料、ECMWF集合预报降水预报数据,通过贝叶斯产品处理技术(Bayesian Processor of Output,BPO)对海河流域内289个格点进行BPO建模,将ECMWF集合成员确定性降水预报修订为贝叶斯降水概率预报,结果显示概率密度峰值较确定性预报更加接近实况;再以51个成员的有效信息得分(Informativeness Score,IS)衡量各集合成员的预报能力,融合各成员的概率预报结果,得到代表ECMWF集合预报不确定性的贝叶斯集成降水概率预报。采用RPS和BS评分方法对海河流域2018年6—8月降水概率预报进行检验,结果表明,在海河流域降水预报中基于BPO方法的贝叶斯集成概率预报评分结果优于集合预报的直接概率预报结果,为海河流域降水概率预报业务奠定了基础。
Persistent Heavy Rainfall (PHR) is the most influential extreme weather event in Asian summer, which has attracted intensive interests of many scientists. By use of operational global ensemble forecasts from China Meteorological Administration(CMA), a new verification method applied to evaluate the predictability of PHR is investigated. A metrics called Index of Composite Predictability (ICP) established on very basic verification indicators, in this paper, Equitable Threat Score(ETS) of 24h accumulated precipitation and Root Mean Square Error(RMSE) of Height at 500hPa is Selected to distinguish “good” and “poor” prediction from all ensemble members. Using the metrics of ICP, the predictability of two typical PHR events in June 2010 and June 2011 is estimated. The results show that the “good member” and “poor member” can be identified by ICP and present an obvious discrepancy in predicting the key weather system which impact on PHR. The different performance of “Good member” and “Poor member” reveals the higher predictability both in synoptic scale and mesoscale weather system in their location, duration and the movement by “Good member”. The source of growth errors for “Poor” member is mainly from errors of initial conditions in northern polar region. The growth of perturbation errors and the reason to cause the better or worse performance of ensemble member also have great value for future model improvement and further research. 相似文献