基于地面资料集合均方根滤波同化方案的京津冀暴雨模拟研究
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摘要
为了更加有效地同化地面自动气象站观测资料,针对模式地形与观测站地形存在的高度差异对同化效果的影响,提出了相应的解决方案。在同化系统的位温和露点观测误差中分别引入位温和露点地形代表性误差,在WRF模式中应用集合均方根滤波方法(EnSRF)同化地面自动气象站观测资料,并对2016年一次京津冀暴雨个例进行数值试验。研究结果表明,同化地面资料后,同化阶段的均方根误差、预报阶段的降水TS评分和前13个时次各要素预报均有整体改进。在观测误差中引入地形代表性误差与引入前相比,风场均方根误差得到整体改进;位温和露点的均方根误差在前期表现并不稳定,在后期有所改进;预报阶段前24 h累计降水与后24 h累计降水TS评分在整体上均有所提高。新方案能够减少高度差异对同化效果的影响。
In order to more efficiently assimilate surface Automatic Weather Station(AWS) data, a new scheme based on the Ensemble Square Root Filter(EnSRF) is proposed for further improvement via solving the negative impact of assimilation results caused by elevation differences between observation sites and the model surface. Terrain Error of Representativeness(TER) for potential temperature and dewpoint temperature are added to temperature and dewpoint temperature errors of surface observation data assimilation in the WRF-EnSRF system, and a numerical simulation of a heavy rain event in Beijing-Tianjin-Hebei region in 2016 has been conducted. Results show that the root mean square error(RMSE), the threat score(TS) and various elements simulated in the first 13 h generally have been improved. With the TER being added, the RMSE of the wind field is improved in general, whereas the RMSE of potential temperature and dewpoint temperature are unstable in the earlier stage but they are improved in the later stage; TS of the first 24 h and the 24-48 h accumulated rainfall are overall improved compared with the results without TER. Thereby, the new scheme is able to reduce the negative impact of assimilation results caused by elevation differences.
In order to more efficiently assimilate surface Automatic Weather Station(AWS) data, a new scheme based on the Ensemble Square Root Filter(EnSRF) is proposed for further improvement via solving the negative impact of assimilation results caused by elevation differences between observation sites and the model surface. Terrain Error of Representativeness(TER) for potential temperature and dewpoint temperature are added to temperature and dewpoint temperature errors of surface observation data assimilation in the WRF-EnSRF system, and a numerical simulation of a heavy rain event in Beijing-Tianjin-Hebei region in 2016 has been conducted. Results show that the root mean square error(RMSE), the threat score(TS) and various elements simulated in the first 13 h generally have been improved. With the TER being added, the RMSE of the wind field is improved in general, whereas the RMSE of potential temperature and dewpoint temperature are unstable in the earlier stage but they are improved in the later stage; TS of the first 24 h and the 24-48 h accumulated rainfall are overall improved compared with the results without TER. Thereby, the new scheme is able to reduce the negative impact of assimilation results caused by elevation differences.
引文
邵长亮,闵锦忠.2015.集合均方根滤波同化地面自动站资料的技术研究.大气科学,39(1):1-11.Shao C L,Min J Z.2015.A study of the assimilation of surface automatic weather station data using the ensemble square root filter.Chinese J Atmos Sci,39(1):1-11 (in Chinese)
徐枝芳,龚建东,王建捷等.2007.复杂地形下地面观测资料同化Ⅱ:模式地形与观测站地形高度差异代表性误差.大气科学,31(3):449-458.Xu Z F,Gong J D,Wang J J,et al.2007.A study of assimilation of surface observational data in complex terrain part Ⅱ:Representative error of the elevation difference between model surface and observation site.Chinese J Atmos Sci,31(3):449-458 (in Chinese)
Benjamin S G,Dévényi D,Weygandt S S,et al.2004.An hourly assimilation-forecast cycle:The RUC.Mon Wea Rev,132(2):495-518
Chen X C,Zhao K,Sun J Z,et al.2016.Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case.Adv Atmos Sci,33(10):1106-1119
Deng X X,Stull R.2005.A mesoscale analysis method for surface potential temperature in mountainous and coastal terrain.Mon Wea Rev,133(2):389-408
Devenyi D,Benjamin S G.2003.A variational assimilation technique in a hybrid isentropic-sigma coordinate.Meteor Atmos Phys,82(1-4):245-257
Fujita T,Stensrud D J,Dowell D C.2006.Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties.Mon Wea Rev,135(5):1846-1868
Gaspari G,Cohn S E.1999.Construction of correlation functions in two and three dimensions.Quart J Roy Meteoro Soc,125(554):723-757
Guo Y R,Shin D H,Lee J H,et al.2002.Application of the MM5 3DVAR system for a heavy rain case over the Korean peninsula//Papers Presented at the Twelfth PSU/NCAR Mesoscale Model Users′ Workshop NCAR.Boulder
Lazarus S M,Ciliberti C M,Horel J D,et al.2002.Near-real-time applications of a mesoscale analysis system to complex Terrain.Wea Forecasting,17(5):971-1000
Lee S J,Parrish D F,Wu W S.2005.Near-surface data assimilation in the NCEP Gridpoint Statistical-Interpolation System:Use of land temperature data and a comprehensive forward model.Office Note,446
Miller P A,Benjamin S G.1992.A system for the hourly assimilation of surface observations in mountainous and flat terrain.Mon Wea Rev,120(10):2342-2359
Myrick D T,Horel J D,Lazarus S M.2005.Local adjustment of the background error correlation for surface analyses over complex terrain.Wea Forecasting,20(2):149-160
Pu Z X,Zhang H L,Anderson J.2013.Ensemble Kalman filter assimilation of near-surface observations over complex terrain:Comparison with 3DVAR for short-range forecasts.Tellus A:Dyn Meteor Oceanogr,65(1):19620
Ruggiero F H,Sashegyi K D,Madala R V,et al.1996.The use of surface observations in four-dimensional data assimilation using a mesoscale model.Mon Wea Rev,124(5):1018-1033
Stensrud D J,Yussouf N,Dowell D C,et al.2009.Assimilating surface data into a mesoscale model ensemble:Cold pool analyses from spring 2007.Atmos Res,93(1-3):207-220
Tong M J,Xue M.2005.Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model:OSS experiments.Mon Wea Rev,133(7):1789-1807
Urban B.1996.Coherent observation operators for surface data assimilation with application to snow depth.J Appl Meteor,35(2):258-270
徐枝芳,龚建东,王建捷等.2007.复杂地形下地面观测资料同化Ⅱ:模式地形与观测站地形高度差异代表性误差.大气科学,31(3):449-458.Xu Z F,Gong J D,Wang J J,et al.2007.A study of assimilation of surface observational data in complex terrain part Ⅱ:Representative error of the elevation difference between model surface and observation site.Chinese J Atmos Sci,31(3):449-458 (in Chinese)
Benjamin S G,Dévényi D,Weygandt S S,et al.2004.An hourly assimilation-forecast cycle:The RUC.Mon Wea Rev,132(2):495-518
Chen X C,Zhao K,Sun J Z,et al.2016.Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case.Adv Atmos Sci,33(10):1106-1119
Deng X X,Stull R.2005.A mesoscale analysis method for surface potential temperature in mountainous and coastal terrain.Mon Wea Rev,133(2):389-408
Devenyi D,Benjamin S G.2003.A variational assimilation technique in a hybrid isentropic-sigma coordinate.Meteor Atmos Phys,82(1-4):245-257
Fujita T,Stensrud D J,Dowell D C.2006.Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties.Mon Wea Rev,135(5):1846-1868
Gaspari G,Cohn S E.1999.Construction of correlation functions in two and three dimensions.Quart J Roy Meteoro Soc,125(554):723-757
Guo Y R,Shin D H,Lee J H,et al.2002.Application of the MM5 3DVAR system for a heavy rain case over the Korean peninsula//Papers Presented at the Twelfth PSU/NCAR Mesoscale Model Users′ Workshop NCAR.Boulder
Lazarus S M,Ciliberti C M,Horel J D,et al.2002.Near-real-time applications of a mesoscale analysis system to complex Terrain.Wea Forecasting,17(5):971-1000
Lee S J,Parrish D F,Wu W S.2005.Near-surface data assimilation in the NCEP Gridpoint Statistical-Interpolation System:Use of land temperature data and a comprehensive forward model.Office Note,446
Miller P A,Benjamin S G.1992.A system for the hourly assimilation of surface observations in mountainous and flat terrain.Mon Wea Rev,120(10):2342-2359
Myrick D T,Horel J D,Lazarus S M.2005.Local adjustment of the background error correlation for surface analyses over complex terrain.Wea Forecasting,20(2):149-160
Pu Z X,Zhang H L,Anderson J.2013.Ensemble Kalman filter assimilation of near-surface observations over complex terrain:Comparison with 3DVAR for short-range forecasts.Tellus A:Dyn Meteor Oceanogr,65(1):19620
Ruggiero F H,Sashegyi K D,Madala R V,et al.1996.The use of surface observations in four-dimensional data assimilation using a mesoscale model.Mon Wea Rev,124(5):1018-1033
Stensrud D J,Yussouf N,Dowell D C,et al.2009.Assimilating surface data into a mesoscale model ensemble:Cold pool analyses from spring 2007.Atmos Res,93(1-3):207-220
Tong M J,Xue M.2005.Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model:OSS experiments.Mon Wea Rev,133(7):1789-1807
Urban B.1996.Coherent observation operators for surface data assimilation with application to snow depth.J Appl Meteor,35(2):258-270