地面和探空资料的EnKF同化对北京7·21极端暴雨模拟的影响
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摘要
针对2012年7月21日北京极端暴雨的业务预报误差,详细地考察地面和探空资料的EnKF同化对7·21极端暴雨总体时空分布和暴雨触发地面特征模拟的影响,进而揭示预报误差的可能原因。结果表明,资料同化显著地改善了北京地区降水时空分布的模拟结果,证实了前人基于观测和敏感性分析提出的"低涡是北京7·21暴雨的关键影响系统"的判断,揭示出低涡对应的地面低压东侧倒槽对北京7·21暴雨的直接贡献。研究结果显示,业务数值模式对此次极端暴雨预报失败的主要原因可能是对低涡和低涡对应的地面低压东侧倒槽强度和位置有较大的预报误差。
Regarding the forecasting errors of operational models for the high-impact extremely heavy rainfall event in Beijing on July 21, 2012, this work examines the impact of assimilating surface and rawinsonde observations using EnKF data assimilation system on the simulation of rainfall distribution and the surface features in the initiation period of the rainfall in Beijing, and reveals the possible reasons for the forecasting errors. Results show that data assimilation significantly improves the simulation of rainfall distribution, confirming that the cyclonic vortex is the key influencing system of the heavy rainfall event, which was proposed by previous researchers based on observations and sensitivity analyses. This work also reveals that the surface low and its associated inverted trough are the direct producers of the rainfall in Beijing. These results indicate that the reason of the failure of the operational models in this extremely heavy rainfall is the large forecasting errors in the strength and location of the cyclonic vortex and the associated inverted trough eastward of the surface low.
Regarding the forecasting errors of operational models for the high-impact extremely heavy rainfall event in Beijing on July 21, 2012, this work examines the impact of assimilating surface and rawinsonde observations using EnKF data assimilation system on the simulation of rainfall distribution and the surface features in the initiation period of the rainfall in Beijing, and reveals the possible reasons for the forecasting errors. Results show that data assimilation significantly improves the simulation of rainfall distribution, confirming that the cyclonic vortex is the key influencing system of the heavy rainfall event, which was proposed by previous researchers based on observations and sensitivity analyses. This work also reveals that the surface low and its associated inverted trough are the direct producers of the rainfall in Beijing. These results indicate that the reason of the failure of the operational models in this extremely heavy rainfall is the large forecasting errors in the strength and location of the cyclonic vortex and the associated inverted trough eastward of the surface low.
引文
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[2]孙继松,何娜,王国荣,等.“7.21”北京大暴雨系统的结构演变特征及成因初探.暴雨灾害,2012,31(3):218-225
[3]方翀,毛冬艳,张小雯,等.2012年7月21日北京地区特大暴雨中尺度对流条件和特征初步分析.气象,2012,38(10):1278-1287
[4]Zhang D,Lin Y,Zhao P,et al.The Beijing extreme rainfall of 21 July 2012:“right results”but for wrong reasons.Geophysical Research Letters,2013,40:1426-1431
[5]王淑莉,康红文,谷湘潜,等.北京7·21暴雨暖区中尺度对流系统的数值模拟.气象,2015,41(5):544-553
[6]孙永刚,孟雪峰,仲夏,等.河套气旋发展东移对一次北京特大暴雨的触发作用.高原气象,2014,33(6):1665-1673
[7]张迎新,李宗涛,姚学祥.京津冀“7.21”暴雨过程的中尺度分析.高原气象,2015,34(1):201-209
[8]Yu H,Meng Z.Key Synoptic-scale features influencing the high-impact heavy rainfall in Beijing,China on 21 July 2012.Tellus A,2016,68:31045
[9]Meng Z,Zhang F.Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part III:comparison with 3DVar in a real-data case study.Mon Wea Rev,2008,136:522-540
[10]Meng Z,Zhang F.Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part IV:comparison with 3DVar in a month-long experiment.Mon Wea Rev,2008,136:3671-3682
[11]Zhang F,Weng Y,Sippel J A,et al.Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter.Mon Wea Rev,2009,137:2015-2125
[12]Skamarock W,Klemp J,Dudhia J,et al.A description of the advanced research WRF version 3[R].NCARTechnical Note TN-475+STR,2008
[13]Grell G,Devenyi D.A generalized approach to parameterizing convection combining ensemble and data assimilation techniques.Geophys Res Lett,2002,29(14):38-1?38-4
[14]Hong S,Dudhia J,Chen S.A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation.Mon Wea Rev,2004,132:103-120
[15]Noh Y,Cheon W G,Hong S Y,et al.Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data.Bound Layer Meteor,2003,107:401-427
[16]Barker D,Huang W,Guo Y,et al.A three-dimensional variational data assimilation system for MM5:implementation and initial results.Mon Wea Rev,2004,132:897-914
[17]Zhang F,Meng Z,Aksoy A.Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part I:perfect model experiments.Monthly Weather Review,2006,134:722-736
[18]Zhang F,Snyder C,Sun J.Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter.Mon Wea Rev,2004,132:1238-1253