TOPOGRAPHY-DEPENDENT HORIZONTAL LOCALIZATION SCALE SCHEME IN GRAPES-MESO HYBRID EN-3DVAR ASSIMILATION SYSTEM
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摘要
Based on the GRAPES-MESO hybrid En-3 DVAR(Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation(from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48 h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3 DVAR, a topography-dependent horizontal localization scale scheme(hereinafter referred to as GRAPES-MESO hybrid En-3 DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3 DVAR, 3 DVAR and GRAPES-MESO hybrid En-3 DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30 h of GRAPES-MESO hybrid En-3 DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error(RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30 h forecast range, that the forecast result of En-3 DVAR-TD-HLS is better than that of the other two systems, but the En-3 DVAR and 3 DVAR are equivalent in terms of their forecast skills. The 36-48 h forecasts of three data assimilation systems have similar forecast skill.
Based on the GRAPES-MESO hybrid En-3 DVAR(Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation(from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48 h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3 DVAR, a topography-dependent horizontal localization scale scheme(hereinafter referred to as GRAPES-MESO hybrid En-3 DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3 DVAR, 3 DVAR and GRAPES-MESO hybrid En-3 DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30 h of GRAPES-MESO hybrid En-3 DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error(RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30 h forecast range, that the forecast result of En-3 DVAR-TD-HLS is better than that of the other two systems, but the En-3 DVAR and 3 DVAR are equivalent in terms of their forecast skills. The 36-48 h forecasts of three data assimilation systems have similar forecast skill.
Based on the GRAPES-MESO hybrid En-3 DVAR(Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation(from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48 h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3 DVAR, a topography-dependent horizontal localization scale scheme(hereinafter referred to as GRAPES-MESO hybrid En-3 DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3 DVAR, 3 DVAR and GRAPES-MESO hybrid En-3 DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30 h of GRAPES-MESO hybrid En-3 DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error(RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30 h forecast range, that the forecast result of En-3 DVAR-TD-HLS is better than that of the other two systems, but the En-3 DVAR and 3 DVAR are equivalent in terms of their forecast skills. The 36-48 h forecasts of three data assimilation systems have similar forecast skill.
引文
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[28]LIU C,XIAO Q,WANG B.An ensemble-based four-dimensional variational data assimilation scheme,Part II:Observing System Simulation Experiments with advanced research WRF(ARW)[J].Mon Wea Rev,2009,137(5):1687-1704.
[29]LIU C,XIAO Q.An ensemble-based four-dimensional variational data assimilation scheme,Part III:Antarctic applications with advanced research WRF using real data[J].Mon Wea Rev,2013,141(8):2721-2739.
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[32]MA Xu-lin,LU Xu,YU Yue-ming,et al.Progress on hybrid ensemble-variational data assimilation numerical weather prediction[J].J Trop Meteor,2014,30(6):1118-1195(in Chinese).
[33]MA Xu-lin,LI Lin-lin,ZHOU Bo-yang,et al.Flow-dependent characteristics of typhoon forecasting errors and optimal coupling coefficient in hybrid data assimilation[J].Trans Atmos Sci,2015,38(6):766-775(in Chinese).
[34]ZHANG Ming-yang,ZHANG Li-feng,ZHANG Bin,et al.Flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation[J].J Meteor Sci,2015,35(6):728-736.
[35]ZHU Hao-nan,MIN Jin-zhong,DU Ning-zhu,et al.Implementation and testing of a hybrid back and forth nudging ensemble Kalman filter(HBFNEnKF)data assimilation method[J].Chin J Atmos Sci,2016,40(5):995-1008(in Chinese).
[36]SHEN Fei-fei,MIN Jin-zhong,XU Dong-mei,et al.Assimilation of Doppler radar velocity observations with hybrid ETKF-3DVAR method,Part I:Experiments with simulated data[J].Trans Atmos Sci,2016,39(1):81-89(in Chinese).
[37]CHEN Liang-lv,CHEN Jing,XUE Ji-shan,et al.Development and testing of the GRAPES regional ensemble-3DVAR hybrid data assimilation system[J].JMeteor Res,2015,29(6):981-996(in Chinese).
[38]ZHANG Han-bin,CHEN Jing,ZHI Xie-fei,et al.Study on the Application of GRAPES Regional Ensemble Prediction System[J].Meteor Mon,2014,40(9):1076-1085(in Chinese).
[39]ZHANG Han-bin,ZHI Xie-fei,CHEN Jing,et al.Study of the modification of multi-model ensemble scheme for tropical cyclone forecast[J].J Trop Meteor,2015,21(4):389-399.
[40]ZHANG Han-bin,CHEN Jing,ZHI Xie-fei,et al.Design and comparison of perturbation schemes for GRAPES_Meso based ensemble forecast[J].Trans Atmos Sci,2014,37(3):276-384(in Chinese).
[41]HOUTEKAMER P L,MITCHELL H L.Data assimilation using an ensemble Kalman filter technique[J].Mon Wea Rev,1998,126(3):796-811.
[42]HOUTEKAMER P L,MITCHELL H L.A sequential ensemble Kalman filter for atmospheric data assimilation[J].Mon Wea Rev,2001,129(1):123-137.
[43]GASPARI G,COHN S.Construction of correlation functions in two and three dimensions[J].Quart J Roy Meteor Soc,1999,125(554):723-757.
[44]OTT E,HUNT B R,SZUNYOGH I,et al.Exploiting local low dimensionality of the atmospheric dynamics for efficient ensemble Kalman filtering[R].San Francisco:American Geophysical Union,2002:12.
[45]OTT E,HUNT B R,SZUNYOGH I,et al.A local ensemble Kalman filter for atmospheric data assimilation[J].Tellus,2004,56(A):415-428.
[46]HUNT B R,KOSTELICH E J,SZUNYOGH I.Efficient data assimilation for spatiotemporal chaos:A local ensemble transform Kalman filter[J].Physical Data-an,2007,230(1-2):112-126.
[47]MIYOSHI T,YAMANE S,ENOMOTO T.Localizing the error covariance by physical distances within a local ensemble transform Kalman filter(LETKF)[J].SOLA,2007,3(27):89-92.
[48]SZUNYOGH I,KOSTELICH E J,GYARMATI G,et al.A local ensemble transform Kalman filter data assimilation system for the NCEP global model[J].Tellus,2008,60(A):113-130.
[49]LIU Shuo,MIN Jin-zhong.The study of adaptive localization and adaptive covariance inflation for WRF-EnSRF Assimilation System[D].Nanjing:Nanjing University of Information Science&Technology,2012.
[2]RABIER F,THEPAUT J N,COURTIER P.Extend assimilation and forecast experiment with four-dimensional variational assimilation system[J].Quart J Roy Meteor Soc,1998,124(550):1861-1887.
[3]BARKER D M,HUANG W,GUO Y R,et al.Athree-dimensional(3DVAR)data assimilation system for use with MM5:Implementation and initial result[J].Mon Wea Rev,2004,132(4):897-914.
[4]ANDERSON J L.An ensemble adjustment Kalman filter for data assimilation[J].Mon Wea Rev,2001,129(12):2884-2903.
[5]WHITAKER J S,HAMIL T M.Ensemble data assimilation without perturbed observation[J].Mon Wea Rev,2002,130(7):1913-1924.
[6]SNYDER C,ZHANG F.Test of an ensemble Kalman filter for convective-scale data assimilation[J].Mon Wea Rev,2003,131(26):1663-1677.
[7]TONG M,XUE M.Ensemble Kalman filter assimilation of Doppler radar data with a compressible non-hydrostatic model:OSS experiments[J].Mon Wea Rev,2005,133(7):1789-1807.
[8]ZHANG F,MENG Z,AKSOY A.Test of ensemble Kalman filter for mesoscale and regional scale data assimilation,Part I:Perfect-model experiment[J].Mon Wea Rev,2006,134(2):722-736.
[9]TORN R D,HAKIM G J,SNYDER C.Boundary conditions for a limited-area ensemble Kalman filter[J].Mon Wea Rev,2006,134(9):2490-2502.
[10]MENG Z,ZHANG F.Test of ensemble Kalman filter for mesoscale and regional scale data assimilation,Part II:Imperfect-model experiments[J].Mon Wea Rev,2007,135(4):1403-1423.
[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[J].Mon Wea Rev,2009,137(7):2105-2125.
[12]HAMILL T M,SNYDER C,MORSS R E.Analysis-error statistics of a quasi-geostrophic model using three-dimensional variational assimilation[J].Mon Wea Rev,2002,130(11):2777-2790.
[13]WANG X.Incorporating ensemble covariance in the Gridpoint Statistical Interpolation(GSI)variational minimization:A mathematical framework[J].Mon Wea Rev,2010,138(7):2990-2995.
[14]WANG X,PARRISH D,KLEIST D,et al.GSI3DVAR-based ensemble-variational hybrid data assimilation for NCEP global forecast system:single-resolution experiments[J].Mon Wea Rev,2013,141(11):4098-4117.
[15]WANG X,BARKER D,SNYDER C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRFmodel,Part I:Observing system simulation experiment[J].Mon Wea Rev,2008,136(12):5116-5131.
[16]WANG X.Application of the WRF hybrid ETKF-3DVAR data assimilation system for hurricane track forecasts[J].Wea Forecast,2011,26(6):868-884.
[17]WANG X,BARKER D,SNYDER C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRFmodel,Part II:Real observation experiments[J].Mon Wea Rev,2008,136(12):5132-5147.
[18]CLAYTON A M,LORENC A C,BARKER D M.Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office[J].Quart J Roy Meteor Soc,2012,39(675):1445-1461.
[19]COURTIER P,ANDERSSON E,HECKLEY W A,et al..The ECMWF implementation of three-dimensional variational assimilation(3DVAR),Part I:Formulation[J].Quart J Roy Meteor Soc,1998,124(550):1783-1807.
[20]BUEHNER M.Ensemble-derived stationary and flow dependent background error covariances:Evaluation in a quasi-operational NWP setting[J].Quart J Roy Meteor Soc,2005,131(607):1013-1043.
[21]WANG X,C SNYDER,HAMILL T M.On the theoretical equivalence of differently proposed ensemble/VAR hybrid analysis schemes[J].Mon Wea Rev,2007,135(1):222-227.
[22]HAMILL T M,SNYDER C.A hybrid ensemble Kalman filter-3D variational analysis scheme[J].Mon Wea Rev,2000,128(8):2905-2919.
[23]HAMILL T M,WHITAKER J S,FIORINO M,et al.Global ensemble predictions of 2009's tropical cyclones initialized with an ensemble Kalman filter[J].Mon Wea Rev,2011,139(2):668-688.
[24]HAMILL T M,WHITAKER J S,et al.Predictions of2010’s tropical cyclones using the GFS and ensemble-based data assimilation methods[J].Mon Wea Rev,2011,139(10):3243-3247.
[25]LORENC A C.The potential of the ensemble Kalman filter for NWP-a comparison with 4D-VAR[J].Quart JRoy Meteor Soc,2003,129(595):3183-3203.
[26]BUEHNER M.Ensemble-derived stationary and flow dependent background error covariances:Evaluation in a quasi-operational NWP setting[J].Quart J Roy Meteor Soc,2005,131(607):1013-1043.
[27]WANG X,SNYDER C,HAMILL T M.On the theoretical equivalence of differently proposed ensemble/VAR hybrid analysis schemes[J].Mon Wea Rev,2007,135(1):222-227.
[28]LIU C,XIAO Q,WANG B.An ensemble-based four-dimensional variational data assimilation scheme,Part II:Observing System Simulation Experiments with advanced research WRF(ARW)[J].Mon Wea Rev,2009,137(5):1687-1704.
[29]LIU C,XIAO Q.An ensemble-based four-dimensional variational data assimilation scheme,Part III:Antarctic applications with advanced research WRF using real data[J].Mon Wea Rev,2013,141(8):2721-2739.
[30]BUEHNER M,HOUTEKAMER P L,CHARETTE C,et al.Inter-comparison of variational data assimilation and ensemble Kalman filter for global deterministic NWP,Part I:Description and single-observation experiments[J].Mon Wea Rev,2010,138(5):1550-1566.
[31]BUEHNER M,HOUTEKAMER P L,CHARETTE C,et al.Inter-comparison of variational data assimilation and ensemble Kalman filter for global deterministic NWP,Part I:One-month experiments with real observations[J].Mon Wea Rev,2010,138(6):1567-1586.
[32]MA Xu-lin,LU Xu,YU Yue-ming,et al.Progress on hybrid ensemble-variational data assimilation numerical weather prediction[J].J Trop Meteor,2014,30(6):1118-1195(in Chinese).
[33]MA Xu-lin,LI Lin-lin,ZHOU Bo-yang,et al.Flow-dependent characteristics of typhoon forecasting errors and optimal coupling coefficient in hybrid data assimilation[J].Trans Atmos Sci,2015,38(6):766-775(in Chinese).
[34]ZHANG Ming-yang,ZHANG Li-feng,ZHANG Bin,et al.Flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation[J].J Meteor Sci,2015,35(6):728-736.
[35]ZHU Hao-nan,MIN Jin-zhong,DU Ning-zhu,et al.Implementation and testing of a hybrid back and forth nudging ensemble Kalman filter(HBFNEnKF)data assimilation method[J].Chin J Atmos Sci,2016,40(5):995-1008(in Chinese).
[36]SHEN Fei-fei,MIN Jin-zhong,XU Dong-mei,et al.Assimilation of Doppler radar velocity observations with hybrid ETKF-3DVAR method,Part I:Experiments with simulated data[J].Trans Atmos Sci,2016,39(1):81-89(in Chinese).
[37]CHEN Liang-lv,CHEN Jing,XUE Ji-shan,et al.Development and testing of the GRAPES regional ensemble-3DVAR hybrid data assimilation system[J].JMeteor Res,2015,29(6):981-996(in Chinese).
[38]ZHANG Han-bin,CHEN Jing,ZHI Xie-fei,et al.Study on the Application of GRAPES Regional Ensemble Prediction System[J].Meteor Mon,2014,40(9):1076-1085(in Chinese).
[39]ZHANG Han-bin,ZHI Xie-fei,CHEN Jing,et al.Study of the modification of multi-model ensemble scheme for tropical cyclone forecast[J].J Trop Meteor,2015,21(4):389-399.
[40]ZHANG Han-bin,CHEN Jing,ZHI Xie-fei,et al.Design and comparison of perturbation schemes for GRAPES_Meso based ensemble forecast[J].Trans Atmos Sci,2014,37(3):276-384(in Chinese).
[41]HOUTEKAMER P L,MITCHELL H L.Data assimilation using an ensemble Kalman filter technique[J].Mon Wea Rev,1998,126(3):796-811.
[42]HOUTEKAMER P L,MITCHELL H L.A sequential ensemble Kalman filter for atmospheric data assimilation[J].Mon Wea Rev,2001,129(1):123-137.
[43]GASPARI G,COHN S.Construction of correlation functions in two and three dimensions[J].Quart J Roy Meteor Soc,1999,125(554):723-757.
[44]OTT E,HUNT B R,SZUNYOGH I,et al.Exploiting local low dimensionality of the atmospheric dynamics for efficient ensemble Kalman filtering[R].San Francisco:American Geophysical Union,2002:12.
[45]OTT E,HUNT B R,SZUNYOGH I,et al.A local ensemble Kalman filter for atmospheric data assimilation[J].Tellus,2004,56(A):415-428.
[46]HUNT B R,KOSTELICH E J,SZUNYOGH I.Efficient data assimilation for spatiotemporal chaos:A local ensemble transform Kalman filter[J].Physical Data-an,2007,230(1-2):112-126.
[47]MIYOSHI T,YAMANE S,ENOMOTO T.Localizing the error covariance by physical distances within a local ensemble transform Kalman filter(LETKF)[J].SOLA,2007,3(27):89-92.
[48]SZUNYOGH I,KOSTELICH E J,GYARMATI G,et al.A local ensemble transform Kalman filter data assimilation system for the NCEP global model[J].Tellus,2008,60(A):113-130.
[49]LIU Shuo,MIN Jin-zhong.The study of adaptive localization and adaptive covariance inflation for WRF-EnSRF Assimilation System[D].Nanjing:Nanjing University of Information Science&Technology,2012.