基于评分最优化的模式降水预报订正算法对比

A Comparison of Optimal-score-based Correction Algorithms of Model Precipitation Prediction

使用2013年1月1日—2016年1月7日全国气象站观测资料,应用准对称混合滑动训练期,不改变雨带预报位置和形态,基于模式降水预报订正结果的TS评分最优化及ETS评分最优化,分别设计最优TS评分订正法(OTS)和最优ETS评分订正法(OETS)确定预报日各级降水订正系数,对2014—2015年降水数值预报进行分级订正,并与频率匹配法(FM)对比。结果表明:在24 h累积降水的多个预报时效订正中,无论是对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和中国气象局的全球模式降水预报,还是对4个模式的简单多模式平均,OTS和OETS较FM在TS评分和ETS评分等传统降水检验指标上均更优秀,其中OTS在所有时效均能提高模式降水预报质量,为三者最优。在概率空间的稳定公平误差评分方面,OTS在各时效、各单模式及多模式平均等方面优势明显。在预报员对应参考时效上,OTS在24~168 h的24 h累积降水预报中的TS评分也优于主观预报。

Based on data from national meteorological stations, one year quasi-symmetrical mixed running training period （QSRTP）, and precipitation prediction from CMA （T639）, ECMWF, NCEP, JMA, both opti- mal threat score （OTS） method and optimal equitable threat score （OETS） method are designed to conduct a comparison experiment on correction algorithms for model precipitation with frequency matching （FM） method. Through classification correction, three methods are used merely to calibrate model precipitation amount with the predicted rain-belt location and shape kept unchanged. The OTS method figures out cor- rection coefficients of different precipitation classes by optimizing threat score （TS） of corrected precipita- tion within training period. OETS is similar to OTS but achieved by optimizing ETS. Correction experi- ments are conducted twice a day with forecast time at 0000 UTC and 1200 UTC, respectively. To consider seasonal background, 20 days before the forecast day and 20 days after the same day in the previous year are adopted to constitute training period. For each national meteorological station, there are 80 samples in total. The correction experiment shows that for either precipitation products of ECMWF, JMA, NCEP, CMA, or their ensemble mean, both OTS and OETS show much better performance than FM in 24 h accu- mulated precipitation classification calibration with different lead time according to traditional verification methods like TS and ETS. In particular, OTS is the best and can improve precipitation prediction in all lead times. After correction, both OTS and OETS incline to forecast larger precipitation area than obser- vation for most classes but less precipitation amounts. Compared to FM, both methods tend to produce a little higher false alarm rates in middle and low classes, which is much less than the reduced missing rate, thereby leading to a higher threat score. In terms of ECMWF correction, OTS and OETS have a relatively stable Bias score of 1.1, although there are much fewer samples in high class. By contrast, FM produces an unstable Bias score, especially in maximum class with score over 2.2, indicating an excessively high missing rate. As for stable equitable error in probability space （SEEPS）, OTS has superiorities over all lead times, all single models and multi-model mean. Furthermore, TS of corrected ECMWF precipitation using OTS method in 2015 are also better than subjective forecast from all aspects, with national averaged threat score of 1 d rainstorm forecast reaching 0. 194.