不同随机物理扰动方案在一次暴雨集合预报中的对比研究
|
摘要
天气预报系统对模式本身的误差非常敏感,尤其是次网格物理参数化过程的不确定性对天气预报系统的准确性具有重要影响。由于风暴尺度系统时间尺度较小、发展剧烈以及高度非线性,传统的中期集合预报方法不再适用。本文将随机物理扰动方案(Stochastic Perturbed Parameterization Tendencies scheme,SPPT)、随机动能补偿方案(Stochastic Kinetic-Energy Backscatter scheme,SKEB)以及混合模式扰动方案(SKEB+SPPT)引入风暴尺度集合预报系统,对2014年5月31日安徽的一次强对流天气过程进行数值模拟,并评估集合预报效果和分析扰动特征及能量变化特征。结果表明:适用于本次天气过程的SPPT方案的时空尺度分别为3 h和60 km;混合模式扰动方案提高了SPPT方案和SKEB方案的集合预报离散度,减小了预报误差,且提高了预报的准确性;混合模式扰动方案减少了SPPT方案和SKEB方案对降水的空报和漏报;混合模式扰动方案的扰动空间分布在预报初期与SPPT方案类似,随着预报时间的推移,其扰动空间形态分布转换为与SKEB方案类似;混合模式扰动方案的扰动动能在所有尺度上都要明显大于其他两种方案,表明两种随机物理扰动方案的结合可以有效弥补两者在不同尺度上的能量缺失。
A weather forecast system is very sensitive to the model error. Particularly,the uncertainty in sub-grid parametrization process has the essential effect on the accuracy of weather forecast system. Due to small time scale,fast and strong nonlinear development of the storm-scale system,the traditional medium-range ensemble forecast method is obsolete. Stochastic Perturbed Parameterization Tendencies( SPPT) scheme,Stochastic Kinetic-Energy Backscatter( SKEB) scheme and mixed model perturbation( SKEB +SPPT) scheme are added to the storm-scale ensemble forecast system,in order to simulate a severe convection weather process in Anhui province on 31 May 2014. This paper evaluates the performance of ensemble forecast and analyses the characteristics of stochastic perturbation and kinetic energy evolvement. Results shows that the 60 km length scale and 3 h decorrelation time scale in SPPT are best in this case. The mixed model perturbation scheme increases( reduces) the spread and accuracy( the forecast error) of SPPT scheme only or SKEB scheme only,and decreases the mistaking and missing forecast of precipitation. The perturbation spatial distribution of the mixed model perturbation scheme is similar to that of the SPPT scheme at the beginning of forecast. As forecast time goes on,the perturbation spatial distribution is transformed and is similar to that of SKEB. The kinetic energy perturbation of the mixed model perturbation scheme is obviously bigger than that of SPPT only or SKEB only in all scales,indicating that the combination of the two stochastic perturbation schemes can efficiently complement the missing energy in different scales.
A weather forecast system is very sensitive to the model error. Particularly,the uncertainty in sub-grid parametrization process has the essential effect on the accuracy of weather forecast system. Due to small time scale,fast and strong nonlinear development of the storm-scale system,the traditional medium-range ensemble forecast method is obsolete. Stochastic Perturbed Parameterization Tendencies( SPPT) scheme,Stochastic Kinetic-Energy Backscatter( SKEB) scheme and mixed model perturbation( SKEB +SPPT) scheme are added to the storm-scale ensemble forecast system,in order to simulate a severe convection weather process in Anhui province on 31 May 2014. This paper evaluates the performance of ensemble forecast and analyses the characteristics of stochastic perturbation and kinetic energy evolvement. Results shows that the 60 km length scale and 3 h decorrelation time scale in SPPT are best in this case. The mixed model perturbation scheme increases( reduces) the spread and accuracy( the forecast error) of SPPT scheme only or SKEB scheme only,and decreases the mistaking and missing forecast of precipitation. The perturbation spatial distribution of the mixed model perturbation scheme is similar to that of the SPPT scheme at the beginning of forecast. As forecast time goes on,the perturbation spatial distribution is transformed and is similar to that of SKEB. The kinetic energy perturbation of the mixed model perturbation scheme is obviously bigger than that of SPPT only or SKEB only in all scales,indicating that the combination of the two stochastic perturbation schemes can efficiently complement the missing energy in different scales.
引文
Berner J,Shutts G J,Leutbecher M,et al.2009.A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system[J].J Atmos Sci,66(3):603-626.
Berner J,Ha S Y,Hacker J P,et al.2011.Model uncertainty in a mesoscale ensemble prediction system:Stochastic versus multiphysics representations[J].Mon Wea Rev,139(6):1972-1995.
Bouttier F,ViéB,Nuissier O,et al.2012.Impact of stochastic physics in a convection-permitting ensemble[J].Mon Wea Rev,140(11):3706-3721.
Bowler N E,Arribas A,Beare S E,et al.2009.The local ETKF and SKEB:Upgrades to the MOGREPS short-range ensemble prediction system[J].Quart J Roy Meteor Soc,135(640):767-776.
Buizza R,Milleer M,Palmer T N.1999.Stochastic representation of model uncertainties in the ECMWF ensemble prediction system[J].Quart J Roy Meteor Soc,125(560):2887-2908.
Duda J D,Wang X,Kong F,et al.2016.Impact of a stochastic kinetic energy backscatter scheme on warm season convection-allowing ensemble forecasts[J].Mon Wea Rev,144(5):1887-1908.
Epstein E S.1969.Stochastic Dynamic Prediction[J].Tellus A,21(6):739-759.
Hamill T M.2001.Interpretation of rank histograms for verifying ensemble forecasts[J].Mon Wea Rev,129(3):550-560.
Hersbach H.2000.Decomposition of the continuous ranked probability score for ensemble prediction systems[J].Wea Forecasting,15(5):559-570.
Leith C E.1974.Theoretical skill of Monte Carlo forecasts[J].Mon Wea Rev,102(6):409-418.
Palmer T N,Buizza R,Doblas-Reyes F,et al.2009.Stochastic parametrization and model uncertainty[C]//ECMWF,Shinfield Park,Reading RG2-9AX,UK,ECMWF Research Department Technical Memorandum,42.
Shutts G.2005.A kinetic energy backscatter algorithm for use in ensemble prediction systems[J].Quart J Roy Meteor Soc,131(612):3079-3102.
Toth Z,Kalnay E.1993.Ensemble forecasting at NMC:The generation of perturbations[J].Bull Amer Meteor Soc,74(12):2317-2330.
Toth Z,Kalnay E.1997.Ensemble forecasting at NCEP and the breeding method[J].Mon Wea Rev,125(12):3297-3319.
ViéB,Nuissier O,Ducrocq V.2011.Cloud-resolving ensemble simulations of mediterranean heavy precipitating events:Uncertainty on initial conditions and lateral boundary conditions[J].Mon Wea Rev,139(2):403-423.
Walser A,Lüthi D,Schr C.2004.Predictability of precipitation in a cloud-resolving model[J].Mon Wea Rev,132(2):560-577.
Wang Y,Bellus M,Geleyn J F,et al.2014.A newmethod for generating initial condition perturbations in a regional ensemble prediction system:Blending[J].Mon Wea Rev,142(5):2043-2059.
高峰,闵锦忠,孔凡铀.2010.基于增长模繁殖法的风暴尺度集合预报试验[J].高原气象,29(2):429-436.Gao Feng,Min Jinzhong,Kong Fanyou.2010.Experiment of the storm-scale ensemble forecast based on breeding of growing mode[J].Plateau Meteor,29(2):429-436.
谭宁,陈静,田华.2013.两种模式随机扰动方案比较及扰动传播分析[J].气象,39(5):543-555.Tan Ning,Chen Jing,Tian Hua.2013.Comparison between two global model stochastic perturbation schemes and analysis of perturbation propagation[J].Meteor Mon,39(5):543-555.
王晨稀,姚建群.2008.对一次局地短时降水的集合预报研究[J].高原气象,27(6):1229-1239.Wang Chenxi,Yao Jianqun.2008.Ensemble forecasting of a local short-lived severe precipitation[J].Plateau Meteor,27(6):1229-1238.
张曼,闵锦忠,戚友存,等.2014.基于KF-ETA积云对流参数化方案集合预报试验[J].高原气象,33(5):1323-1331.Zhang Man,Min Jinzhong,Qi Youcun,et al.2014.Ensemble experiments research based on mass-fluxed cumulus convective parameterization of KF-ETA[J].Plateau Meteor,33(5):1323-1331.DOI:10.7752.j.issn.1000-0534.2013.00086.
庄潇然,闵锦忠,蔡沅辰,等.2016.不同大尺度强迫条件下考虑初始场与侧边界条件不确定性的对流尺度集合预报试验[J].气象学报,74(2):244-258.Zhuang Xiaoran,Min Jinzhong,Cai Yuanchen,et al.2016.Accounting for initial and lateral boundary condition uncertainties under different synoptic-scale forcing in convection-allowing ensemble prediction[J].Acta Meteor Sinica,74(2):244-258.
Berner J,Ha S Y,Hacker J P,et al.2011.Model uncertainty in a mesoscale ensemble prediction system:Stochastic versus multiphysics representations[J].Mon Wea Rev,139(6):1972-1995.
Bouttier F,ViéB,Nuissier O,et al.2012.Impact of stochastic physics in a convection-permitting ensemble[J].Mon Wea Rev,140(11):3706-3721.
Bowler N E,Arribas A,Beare S E,et al.2009.The local ETKF and SKEB:Upgrades to the MOGREPS short-range ensemble prediction system[J].Quart J Roy Meteor Soc,135(640):767-776.
Buizza R,Milleer M,Palmer T N.1999.Stochastic representation of model uncertainties in the ECMWF ensemble prediction system[J].Quart J Roy Meteor Soc,125(560):2887-2908.
Duda J D,Wang X,Kong F,et al.2016.Impact of a stochastic kinetic energy backscatter scheme on warm season convection-allowing ensemble forecasts[J].Mon Wea Rev,144(5):1887-1908.
Epstein E S.1969.Stochastic Dynamic Prediction[J].Tellus A,21(6):739-759.
Hamill T M.2001.Interpretation of rank histograms for verifying ensemble forecasts[J].Mon Wea Rev,129(3):550-560.
Hersbach H.2000.Decomposition of the continuous ranked probability score for ensemble prediction systems[J].Wea Forecasting,15(5):559-570.
Leith C E.1974.Theoretical skill of Monte Carlo forecasts[J].Mon Wea Rev,102(6):409-418.
Palmer T N,Buizza R,Doblas-Reyes F,et al.2009.Stochastic parametrization and model uncertainty[C]//ECMWF,Shinfield Park,Reading RG2-9AX,UK,ECMWF Research Department Technical Memorandum,42.
Shutts G.2005.A kinetic energy backscatter algorithm for use in ensemble prediction systems[J].Quart J Roy Meteor Soc,131(612):3079-3102.
Toth Z,Kalnay E.1993.Ensemble forecasting at NMC:The generation of perturbations[J].Bull Amer Meteor Soc,74(12):2317-2330.
Toth Z,Kalnay E.1997.Ensemble forecasting at NCEP and the breeding method[J].Mon Wea Rev,125(12):3297-3319.
ViéB,Nuissier O,Ducrocq V.2011.Cloud-resolving ensemble simulations of mediterranean heavy precipitating events:Uncertainty on initial conditions and lateral boundary conditions[J].Mon Wea Rev,139(2):403-423.
Walser A,Lüthi D,Schr C.2004.Predictability of precipitation in a cloud-resolving model[J].Mon Wea Rev,132(2):560-577.
Wang Y,Bellus M,Geleyn J F,et al.2014.A newmethod for generating initial condition perturbations in a regional ensemble prediction system:Blending[J].Mon Wea Rev,142(5):2043-2059.
高峰,闵锦忠,孔凡铀.2010.基于增长模繁殖法的风暴尺度集合预报试验[J].高原气象,29(2):429-436.Gao Feng,Min Jinzhong,Kong Fanyou.2010.Experiment of the storm-scale ensemble forecast based on breeding of growing mode[J].Plateau Meteor,29(2):429-436.
谭宁,陈静,田华.2013.两种模式随机扰动方案比较及扰动传播分析[J].气象,39(5):543-555.Tan Ning,Chen Jing,Tian Hua.2013.Comparison between two global model stochastic perturbation schemes and analysis of perturbation propagation[J].Meteor Mon,39(5):543-555.
王晨稀,姚建群.2008.对一次局地短时降水的集合预报研究[J].高原气象,27(6):1229-1239.Wang Chenxi,Yao Jianqun.2008.Ensemble forecasting of a local short-lived severe precipitation[J].Plateau Meteor,27(6):1229-1238.
张曼,闵锦忠,戚友存,等.2014.基于KF-ETA积云对流参数化方案集合预报试验[J].高原气象,33(5):1323-1331.Zhang Man,Min Jinzhong,Qi Youcun,et al.2014.Ensemble experiments research based on mass-fluxed cumulus convective parameterization of KF-ETA[J].Plateau Meteor,33(5):1323-1331.DOI:10.7752.j.issn.1000-0534.2013.00086.
庄潇然,闵锦忠,蔡沅辰,等.2016.不同大尺度强迫条件下考虑初始场与侧边界条件不确定性的对流尺度集合预报试验[J].气象学报,74(2):244-258.Zhuang Xiaoran,Min Jinzhong,Cai Yuanchen,et al.2016.Accounting for initial and lateral boundary condition uncertainties under different synoptic-scale forcing in convection-allowing ensemble prediction[J].Acta Meteor Sinica,74(2):244-258.