[1]刘莹,陈朝平,陈莹,等.基于CMA-REPS小时降水的邻域集合预报应用试验[J].气象研究与应用,2022,43(02):98-104.[doi:10.19849/j.cnki.CN45-1356/P.2022.2.17]
　Liu Ying,Chen Chaoping,Chen Ying,et al.Application experiment of neighborhood ensemble forecasting based on CMA-REPS hourly precipitation[J].Journal of Meteorological Research and Application,2022,43(02):98-104.[doi:10.19849/j.cnki.CN45-1356/P.2022.2.17]
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基于CMA-REPS小时降水的邻域集合预报应用试验()
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气象研究与应用[ISSN:1673-8411/CN:45-1356/P]

卷:
第43卷
期数:
2022年02期
页码:
98-104
栏目:
新技术应用
出版日期:
2022-06-15

文章信息/Info

Title:
Application experiment of neighborhood ensemble forecasting based on CMA-REPS hourly precipitation
作者:
刘莹1; 2;  陈朝平1; 2;  陈莹1; 2;  龙柯吉1; 2;  周秋雪1; 2
1. 四川省气象台, 成都 610072;
2. 高原与盆地暴雨旱涝灾害四川省重点实验室, 成都 610072
Author(s):
Liu Ying1; 2;  Chen Chaoping1; 2;  Chen Ying1; 2;  Long Keji1; 2;  Zhou Qiuxue1; 2
1. Sichuan Meteorological Observatory, Chengdu 610072, China;
2. Sichuan Provincial Key Laboratory of Heavy Rain, Drought and Flood Disasters in Plateaus and Basins, Chengdu 610072, China
关键词:
小时降水; 集合预报; 邻域法
Keywords:
hourly precipitation; ensemble forecast; neighborhood method
分类号:
P456
DOI:
10.19849/j.cnki.CN45-1356/P.2022.2.17
摘要:
基于四川2020年汛期CMA区域集合模式的小时降水产品开展了集合预报邻域法订正试验，试验方案分为四种：邻域平均的集合平均预报（ENM）、邻域平均的集合成员最大预报（MNM）、邻域概率的集合平均概率预报（ENP）、邻域概率的集合最大概率预报（MNP）。结果表明，小时降水超过0.1mm·h-1的ENM在部分预报时效内TS评分略优于原集合平均预报；小时雨强分级检验的结果均显示出邻域概率预报优于原概率预报，随小时降水阈值的增大，最优TS评分越趋于更高邻域半径和更低的概率，且10mm·h-1以上MNP预报更具参考性；MNP能显示集合预报的大范围降水中心，可以从另一个角度给预报员预报提供一定的参考。
Abstract:
Based on the hourly precipitation products of the CMA regional ensemble model in the flood season of Sichuan in 2020, the correction experiment of the ensemble forecast neighborhood method was carried out. There are four kinds of test schemes:neighborhood average ensemble average prediction(ENM), neighborhood average ensemble member maximum prediction(MNM), neighborhood probability ensemble average probability prediction(ENP), and neighborhood probability ensemble maximum probability prediction(MNP). The results show that the TS score of the ENM with hourly precipitation exceeding 0.1mm·h-1 is slightly better than the original ensemble average forecast within the partial forecast period. The results of the hourly rainfall intensity classification test all show that the neighborhood probability prediction is better than the original probability prediction. With the increase of hourly precipitation threshold, the optimal TS score tends to higher neighborhood radius and lower probability, and MNP prediction above 10mm·h-1 is more referential. MNP can display the large-scale precipitation center of ensemble forecast, and can provide a certain reference for forecasters from another angle.

参考文献/References:

[1] 张小雯,唐文苑,郑永光,等.GRAPES_3km数值模式对流风暴预报能力的多方法综合评估[J].气象,2020,46(3):367-380.
[2] 许晨璐,王建捷,黄丽萍.千米尺度分辨率下GRAPES_Meso4.0模式定量降水预报性能评估[J].气象学报,2017, 75(6):851-876.
[3] 曹萍萍,肖递祥,王佳津,等.SWCWARMS及ECMWF模式对四川盆地暖区型和斜压锋生型暴雨预报检验分析[J].高原山地气象研究,2018,38(1):22-29,47.
[4] 张琪,肖递祥,肖红茹, 等.SWCWARMS模式对四川盆地强降水预报的检验分析[J].高原山地气象研究,2019,39(2):23-29.
[5] 陈子通,徐道生,戴光丰,等.热带高分辨率模式(TRAMS-V3.0)技术方案及其系统预报性能[J].热带气象学报,2020,36(4):444-454.
[6] 王静,陈静,钟有亮,等.GRAPES_REPS西南低涡预报检验评估[J].气象,2017,43(4):385-401.
[7] Schwartz C S,Kain J S,Bright D R, et al. Toward Improved Convection-Allowing Ensembles:Model Physics Sensitivities and Optimizing Probabilistic Guidance with Small Ensemble Membership[J]. Weather and Forecasting,2010, 25(1):263-280.
[8] 杜钧, 李俊. 集合预报方法在暴雨研究和预报中的应用[J]. 气象科技进展, 2014, 4(5):6-20.
[9] Theis S E, Hense A, Damrath U. Probabilistic Precipitation Forecasts from a Deterministic Model:A Pragmatic Approach[J]. Meteorological Applications, 2005,12(3):257-268.
[10] Clark A J, Kain J S, Stensrud D J, et al. Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Sonvection-Allowing Ensemble[J]. Monthly Weather Review, 2011, 139(5):1410-1418.
[11] Johnson A, Wang X G. Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble[J]. Monthly Weather Review,2012,140(9):3054-3077.
[12] Bouallègue Z B, Theis S E, Gebhardt C. Enhancing COSMO-DE Ensemble Forecasts by Inexpensive Techniques[J]. Meteorologische Zeitschrift, 2013,22(1):49-59.
[13] Bouallègue Z B, Theis S E. Spatial Techniques Applied to Precipitation Ensemble Forecasts:from Verification Results to Probabilistic Products[J]. Meteorological Applications,2015,21(4):922-929.
[14] 吴志鹏,国兵,张亚萍,等. 基于风暴尺度模式产品的暴雨最优升尺度概率预报方法研究[J].暴雨灾害, 2020,39(4):372-381.
[15] 刘雪晴, 陈静, 陈法敬,等. 降水邻域集合概率方法尺度敏感性试验[J]. 大气科学,2020, 44(2):282-296.
[16] Ebert E. Neighborhood Verification:A Strategy for Rewarding Close Forecasts[J]. Weather and Forecasting, 2009, 24(6):1498-1510.

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备注/Memo

备注/Memo:
收稿日期:2022-01-17。
基金项目:四川省气象局重点实验室重大项目（2018-重点-06）、四川省气象局重点实验室研究型业务面上专项（SCQXKJYJXMS202201）、国家重点研发计划重点专项项目（2017YFC1502000）、（2017YFC1502004）
作者简介:刘莹,工程师,主要从事强对流天气预报和相关技术研究。E-mail:liuyingwy@163.com
通讯作者:陈朝平,正研级高级工程师,主要从事数值预报产品释用和天气预报诊断研究。E-mail:77760543@qq.com
更新日期/Last Update: 1900-01-01