Heavy Precipitation Forecasts Based on Multi-model Ensemble Members

Zhi Xiefei; Zhao Chen

doi:10.11898/1001-7313.20200305

Vol.31, NO.3, 2020

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Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI: 10.11898/1001-7313.20200305.

Citation:

Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI: 10.11898/1001-7313.20200305.

Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI: 10.11898/1001-7313.20200305.

Citation:

Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI: 10.11898/1001-7313.20200305.

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Heavy Precipitation Forecasts Based on Multi-model Ensemble Members

DOI: 10.11898/1001-7313.20200305

Zhi Xiefei^,,
Zhao Chen

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044

Received Date: 2019-10-28
Rev Recd Date: 2020-01-08
Publish Date: 2020-05-31

Abstract

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.
- frequency matching method;
- ensemble forecasts;
- multi-model ensemble;
- forecast errors;
- threat score

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References(31)

Figures and Tables

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

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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

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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

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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

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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

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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

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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

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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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References

[1]	Epstein E S.Stochastic dynamic prediction.Tellus, 1969, 21:739-759. http://d.old.wanfangdata.com.cn/Periodical/tynxb201407013
[2]	Leith C.Theoretical skill of Monte Carlo forecasts.Mon Wea Rev, 1974, 102(6):409-418. doi: 10.1175-1520-0493(1974)102-0409-TSOMCF-2.0.CO%3b2/
[3]	杜钧, 陈静.单一值预报向概率预报转变的基础:谈谈集合预报及其带来的变革.气象, 2010, 36(11):1-11. http://d.old.wanfangdata.com.cn/Periodical/qx201011001
[4]	谭燕, 梁旭东.一次登陆台风的集合预报试验.热带气象学报, 2010, 26(4):401-410. http://d.old.wanfangdata.com.cn/Periodical/rdqxxb201004003
[5]	赵华生, 黄小燕, 黄颖.ECMWF集合预报产品在广西暴雨预报中的释用.应用气象学报, 2018, 29(3):344-353. doi: 10.11898/1001-7313.20180308
[6]	Krishnamurti T N, Kishtawal C M, LaRow T E, et al.Improved weather and seasonal climate forecasts from multimodel superensemble.Science, 1999, 285:1548-1550. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ021123302/
[7]	Krishnamurti T N, Kishtawal C M, Zhang Z, et al.Multimodel Ensemble forecasts for weather and seasonal climate.J Climate, 2000, 13(23):4196-4216. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_dd3ceacf8af25a2c9971b348675d2e65
[8]	Krishnamurti T N, Kishtawal C M, Shin D W, et al.Improving tropical precipitation forecasts from a multianalysis superensemble.J Climate, 2000, 13(23):4217-4227. http://cn.bing.com/academic/profile?id=5504a17d6c5ec3fbe7cf2d158371859c&encoded=0&v=paper_preview&mkt=zh-cn
[9]	赵声蓉.多模式温度集成预报.应用气象学报, 2006, 17(1):52-58. http://qikan.camscma.cn/jamsweb/article/id/20060109
[10]	Krishnamurti T N, Gnanaseelan C, Chakraborty A.Prediction of the diurnal change using a multimodel superensemble.Part Ⅰ:Precipitation.Mon Wea Rev, 2007, 135(10):3613-3632. doi: 10.1175-MWR3446.1/
[11]	林春泽, 智协飞, 韩艳, 等.基于TIGGE资料的地面气温多模式超级集合预报.应用气象学报, 2009, 20(6):706-712. http://qikan.camscma.cn/jamsweb/article/id/20090608
[12]	刘长征, 杜良敏, 柯宗建, 等.国家气候中心多模式解释应用集成预测.应用气象学报, 2013, 24(6):677-685. http://qikan.camscma.cn/jamsweb/article/id/20130604
[13]	智协飞, 赵欢, 朱寿鹏, 等.基于CMIP5多模式回报资料的地面气温超级集合研究.大气科学学报, 2016, 39(1):64-71. http://d.old.wanfangdata.com.cn/Periodical/njqxxyxb201601008
[14]	尹忠海, 张沛源.利用卡尔曼滤波校准方法估算区域降水量.应用气象学报, 2005, 16(2):213-219. http://qikan.camscma.cn/jamsweb/article/id/20050226
[15]	卞赟, 智协飞, 李佰平.多模式集成方法对延伸期降水预报的改进.中国科技论文, 2015, 10(15):1813-1817. http://d.old.wanfangdata.com.cn/Periodical/zgkjlwzx201515014
[16]	He C F, Zhi X F, Fraedrich K, et al.Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method.Meteorol Atmos Phys, 2015, 127(4):467-479. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=480299c20ed3ce95fb6265cb790779bb
[17]	颜妍, 周任君, 柯宗建, 等.基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验.中国科学技术大学学报, 2017, 47(5):392-402. http://d.old.wanfangdata.com.cn/Periodical/zgkxjsdxxb201705004
[18]	Du J, Mullen S L.Short-range ensemble forecasting of quantitative precipitation.Mon Wea Rev, 1997, 125(10):2427-2459. doi: 10.1175-1520-0493(1997)125-2427-SREFOQ-2.0.CO%3b2/
[19]	马清, 龚建东, 李莉.超级集合预报的误差订正与集成研究.气象, 2008, 34(3):42-48. http://d.old.wanfangdata.com.cn/Periodical/qx200803007
[20]	邓国, 龚建东, 邓莲堂, 等.国家级区域集合预报系统研发和性能检验.应用气象学报, 2010, 21(5):513-523. http://qikan.camscma.cn/jamsweb/article/id/20100501
[21]	宇婧婧, 沈艳, 潘旸, 等.概率密度匹配法对中国区域卫星降水资料的改进.应用气象学报, 2013, 24(5):544-553. http://qikan.camscma.cn/jamsweb/article/id/20130504
[22]	孙靖, 程光光, 张小玲.一种改进的数值预报降水偏差订正方法及应用.应用气象学报, 2015, 26(2):173-184. doi: 10.11898/1001-7313.20150205
[23]	智协飞, 季晓东, 张璟.基于TIGGE资料的地面气温和降水的多模式集成预报.大气科学学报, 2013, 36(3):257-266. http://d.old.wanfangdata.com.cn/Periodical/njqxxyxb201303001
[24]	吴启树, 韩美, 刘铭, 等.基于评分最优化的模式降水预报订正算法对比.应用气象学报, 2017, 28(3):306-317. doi: 10.11898/1001-7313.20170305
[25]	Zhu Y, Luo Y.Precipitation calibration based on the frequency-matching method.Wea Forecasting, 2013, 30(5):1109-1124. doi: 10.1175/WAF-D-13-00049.1
[26]	Park Y Y, Buizza R, Leutbecher M.TIGGE:Preliminary results on comparing and combining ensembles.Q J Roy Meteor Soc, 2010, 134(637):2029-2050. http://d.old.wanfangdata.com.cn/Periodical/yyqxxb200906008
[27]	Bougeault P, Coauthors.The THORPEX Interactive Grand Global Ensemble.Bull Am Meteorol Soc, 2010, 91(8):1059-1072. http://d.old.wanfangdata.com.cn/Periodical/skxjz201902004
[28]	Xie P, Xiong A Y.A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses.J Geophys Res Atmos, 2011, 116:D21106. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7fb48268dc6634d4bf61e9c2f283a2db
[29]	智协飞, 黄闻.基于卡尔曼滤波的中国区域气温和降水的多模式集成预报.大气科学学报, 2019, 42(2):39-48. http://d.old.wanfangdata.com.cn/Periodical/njqxxyxb201902004
[30]	李俊, 杜钧, 陈超君."频率匹配法"在集合降水预报中的应用研究.气象, 2015, 41(6):674-684. http://d.old.wanfangdata.com.cn/Periodical/qx201506002
[31]	Candille G, Talagrand O.Evaluation of probabilistic prediction systems for a scalar variable.Q J Roy Meteor Soc, 2005, 131(609):2131-2150. doi: 10.1256-qj.04.71/