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Heavy Precipitation Forecasts Based on Multi-model Ensemble Members
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摘要: 基于TIGGE资料集下欧洲中期天气预报中心（ECMWF）、日本气象厅（JMA）、英国气象局（UKMO）、美国国家环境预报中心（NCEP）和中国气象局（CMA）5个气象预报中心2016年5月1日—8月31日中国地区逐日起报预报时效为24~168 h的24 h累积降水量集合预报的结果，对各个集合预报成员进行了频率匹配法的订正，并对订正前后的多模式集成预报效果进行评估。结果表明：采用频率匹配法订正后的降水预报，有效改善了集合平均预报中强降水（日降水量25 mm以上）预报由平滑作用产生的量级偏小现象，使预报的降水量级更接近实况，但对降水落区预报改进不明显。基于卡尔曼滤波技术的集成预报效果优于基于线性回归的超级集合预报和消除偏差集合平均预报，对强降水落区的预报较单模式更优。基于集合成员订正的降水多模式集成预报在强降水的落区预报和降水中心的量级预报更接近实况，效果优于原始多模式集成预报与单模式结果。Abstract: Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes.
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图 1 2016年8月12日华南地区(18°~30°N, 102°~120°E) 24 h累积降水量分布的实况值和时效为24 h的KF，SUP，BREM，ECMWF，UKMO及JMA预报
Fig. 1 Distributions of 24 h accumulated precipitation in observation and forecast with lead time of 24 h for KF, SUP, BREM, ECMWF, UKMO and JMA over South China (18°-30°N, 102°-120°E) on 12 Aug 2016
图 2 预报期内中国区域3种多模式集成方法和ECMWF，UKMO及JMA 3个模式24 h累积降水的区域平均误差绝对值(a)和大雨的TS评分(b)
Fig. 2 Regional mean absolute error of 24 h accumulated precipitation(a) and threat score(TS) of heavy rain(b) for KF, SUP, BREM, ECMWF, UKMO and JMA during forecasting period
图 3 对ECMWF, JMA和UKMO预报时效为168 h集合成员采用频率匹配法进行订正前后Talagrand分布
Fig. 3 Taragrand distribution of precipitation with 168 h lead time in ECMWF, JMA and UKMO before and after calibrated by FMM
图 4 2016年5—8月中国区域24 h累积降水量ECMWF, JMA, UKMO, NCEP和CMA订正前(虚线)及订正后(实线)不同预报时效的小雨、中雨、大雨、暴雨的TS评分
Fig. 4 TS of light rain, moderate rain, heavy rain and rainstorm for 24 h accumulated precipitation with lead time from 24 h to 168 h for ECMWF, JMA, NCEP, UKMO and CMA over China from May to Aug in 2016 before(the dashed line) and after(the solid line) FMM calibration
图 5 预报期内中国区域24 h累积降水量的多模式集成方法FMM_KF，KF，BREM及FMM_UK和模式UKMO及ECMWF不同预报时效的小雨、中雨、大雨、暴雨和大暴雨的TS评分
Fig. 5 TS of light rain, moderate rain, heavy rain, rainstorm and heavy rainstorm for 24 h accumulated precipitation with different lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF over China during forecasting period
图 6 预报期内中国区域24 h累积降水量的多模式集成方法FMM_KF，KF，BREM及FMM_UK和模式UKMO及ECMWF不同预报时效的大雨、暴雨和大暴雨的空报率(FAR)以及漏报率(MR)
Fig. 6 The FAR and MR of heavy rain, rainstorm and heavy rainstorm for 24 h accumulated precipitation with different lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF over China during forecasting period
图 7 预报期内中国区域24 h累积降水量的多模式集成方法FMM_KF，KF，BREM及FMM_UK和模式UKMO及ECMWF不同预报时效空间平均的均方根误差(a)以及距平相关系数(b)
Fig. 7 Regional averaged root mean square error(a) and anomaly correlation coefficient(b) of 24 h accumulated precipitation with lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF during forecasting period
图 8 2016年8月17日的华南及南海区域(15°~25°N，105°~122°E)24 h累积降水量实况以及24 h预报时效的FMM_KF, KF, BREM, FMM_UK和UKMO预报的分布
Fig. 8 The distribution of 24 h accumulated precipitation in observation and forecast with 24 h lead time for FMM_KF, KF, BREM, FMM_UK, UKMO over South China and South China Sea(15°-25°N, 105°-122°E) on 17 Aug 2016
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