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A Comparison of Optimal-score-based Correction Algorithms of Model Precipitation Prediction
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摘要: 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。Abstract: 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 optimal 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 correction coefficients of different precipitation classes by optimizing threat score (TS) of corrected precipitation within training period. OETS is similar to OTS but achieved by optimizing ETS. Correction experiments 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 accumulated 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 observation 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.
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图 1 TS评分或ETS评分提高的不同情况示意图
(a) 预报站点数与实况相同，(b) 预报站点数多于实况，(c) 预报站点数少于实况
Fig. 1 qIllustration of different conditions of TS or ETS performance improvement
(a) the number of forecast stations is in consistent with the observation, (b) the number of forecast stations is larger than the observation, (c) the number of forecast stations is less than the observation
图 3 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法订正的24 h累积降水24 h预报的TS评分 (a)、ETS评分 (b)、空报率 (c)、漏报率 (d)、预报偏差 (e) 和HSS技巧评分 (f)
Fig. 3 TS (a), ETS (b), false alarm rate (c), miss rate (d), Bias (e) and HSS (f) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time of 24 h during 2014-2015
图 4 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法订正的0.1 mm (a)，10 mm (b)，25 mm (c) 和50 mm (d)24 h累积降水24~240 h预报的TS评分
Fig. 4 TS of 0.1 mm (a), 10 mm (b), 25 mm (c) 和50 mm (d) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015
图 5 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法的24 h累积降水24~240 h预报的SEEPS技巧评分
Fig. 5 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015
图 6 2014—2015年JMA (a)、NCEP (b) 和T639(c) 降水预报及FM，OTS，OETS3种算法订正的24 h累积降水24 h预报TS评分
Fig. 6 TS of 24 h accumulative precipitation by FM, OTS and OETS based on JMA (a), NCEP (b) and T639(c) with lead time of 24 h during 2014-2015
图 7 2014—2015年简单多模式降水预报平均和FM，OTS，OETS 3种算法订正的24 h累积降水24 h预报TS评分
Fig. 7 TS of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time of 24 h during 2014-2015
图 8 2014—2015年简单多模式降水预报平均和FM，OTS，OETS 3种算法订正的24 h累积降水24~72 h预报SEEPS技巧评分
Fig. 8 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time from 24 h to 72 h during 2014-2015
表 1 2014—2015年FM，OTS和OETS 3种算法对JMA，NCEP及T639降水预报订正的24 h累积降水24~72 h预报的SEEPS技巧评分
Table 1 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on JMA, NCEP and T639 with lead time from 24 h to 72 h during 2014-2015
模式 算法 24 h预报 48 h预报 72 h预报 JMA 未订正 0.562 0.506 0.466 FM 0.626 0.562 0.504 OTS 0.640 0.572 0.513 OETS 0.632 0.567 0.511 NCEP 未订正 0.525 0.493 0.474 FM 0.572 0.533 0.495 OTS 0.594 0.556 0.526 OETS 0.588 0.550 0.519 T639 未订正 0.512 0.472 0.405 FM 0.567 0.518 0.425 OTS 0.602 0.546 0.453 OETS 0.599 0.541 0.451 
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表 2 2015年中国国家气象中心预报员与ECMWF_OTS 24 h累积降水TS评分对比
Table 2 TS comparisons of 24 h accumulative precipitation between China NMC forecasters and ECMWF_OTS all over China stations in 2015
起报时间 预报时效/h 小雨 中雨 大雨 暴雨 预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS 24 0.594 0.608 0.395 0.414 0.281 0.302 0.175 0.189 48 0.572 0.590 0.359 0.376 0.250 0.263 0.147 0.153 72 0.552 0.565 0.328 0.335 0.225 0.233 0.118 0.136 00:00 96 0.522 0.538 0.298 0.305 0.198 0.206 0.094 0.118 120 0.500 0.514 0.271 0.276 0.168 0.178 0.073 0.093 144 0.473 0.486 0.242 0.244 0.145 0.148 0.080 0.083 168 0.442 0.448 0.208 0.214 0.124 0.133 0.050 0.062 24 0.590 0.609 0.392 0.414 0.285 0.304 0.186 0.199 12:00 48 0.574 0.584 0.354 0.372 0.255 0.264 0.158 0.163 72 0.550 0.559 0.323 0.337 0.222 0.236 0.125 0.150
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