基于BGM框架的短期集合预报扰动典型规律研究
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摘要
采用增长模培育(Breeding of Growing Modes,BGM)法开展有限区域模式短期集合预报研究,亟需解决的问题是集合预报扰动的发展及演变。因此论文结合经典的适时缩放培育思想,利用增长模培育法,基于WRF3.6模式(采用WRF-ARW),开发和构建了一个包含水平风场、垂直速度、位温扰动、位势扰动和水汽混合比共6个基本物理量的区域短期集合预报系统(WRF-EPS)。在此基础上,以2016年6月整月我国南方大范围暴雨为样例,针对扰动发展与演变的典型问题进行了探讨。试验结果表明:1)模式大气高、中、低三层的物理量扰动增长可以分为两个阶段,第一阶段为扰动快速线性增长,该阶段内扰动快速完成全部涨幅;第二阶段为非线性稳定阶段,从快速线性增长过渡到非线性稳定阶段大约需要24 h。2)各物理量的扰动增长率、相关系数以及增长模进入非线性稳定阶段的时间大致相同,但对于同一等压面不同物理量或同一物理量不同等压面,每个参数达到非线性稳定后的数值大小及演变规律存在差异,且随时间演变均伴有日内振荡现象。3)对于扰动振幅相同但初始随机模态不同的初值集合,不同随机模态对扰动培育的影响主要是在扰动的非线性稳定阶段,而在快速的线性增长阶段,它们之间的差异很小。4)对于初始随机模态相同但振幅不同的初值集合,不同扰动振幅对扰动演变的影响主要是在扰动的快速线性增长阶段,而在非线性稳定阶段,它们之间的差异很小,并且不同初始振幅对扰动进入非线性稳定阶段的时间基本没有影响。
When one makes short-range regional ensemble forecast by using the Breeding of Growing Modes(BGM)method,a critical problem first to be faced is what the typical characteristics of evolution of initial perturbations are in the short-range ensemble prediction system(EPS). Consequently,a short-range EPS based on the BGMmethod has been developed with WRF3. 6.The regular rescaling scheme has also been incorporated into this system.Meanwhile,the short-range EPS that has covered the uncertainties of horizontal wind,vertical velocity,potential temperature,geopotential height and water vapor mixture ratio takes the large-range rainstorm in southern China in June 2016 as an example to recognize the evolving mechanism of perturbations. The results showthat:(1) the perturbation growing process of physical quantities in the upper,middle and lower levels of model atmosphere can be divided into two stages,one of which is the rapid linear growth of perturbations and the perturbations quickly complete the total increase of themselves in this phase,another of which is the nonlinear stable phase of perturbations growing and the transition from the fast linearly growing phase to the nonlinear stable phase takes about 24 h.(2) The perturbations of physical quantities take approximately the same length of time to enter the nonlinear stable phase through the temporal evolution features of perturbation growth rate,correlation coefficient and perturbation growing modes.Nonetheless,when the perturbations come into the nonlinear stable stage,the numerical values and evolving characteristics of each assessment parameter are different for the same pressure level with different physical quantities or the same physical quantity at different pressure levels.Moreover,there is a diurnal oscillation phenomenon with time for each assessment parameter at the nonlinear stable stage.(3) For the initial ensemble of different random patterns with the same size of perturbation amplitude,the impacts of different random patterns on perturbations breeding mainly yield differences in the nonlinear stable stage while the differences between each pattern are too small to distinguish in the fast linearly growing stage.(4) For the initial ensemble of the same random pattern but with different sizes of perturbation amplitudes,the influences of different amplitudes on the evolution of perturbations mainly occurs in the fast linear growth phase,while the differences between each amplitude are quite small in the nonlinear stable phase.Additionally,the different sizes of initial amplitudes have no influence on the characteristic time scale of the perturbations getting into the nonlinear stable phase.
When one makes short-range regional ensemble forecast by using the Breeding of Growing Modes(BGM)method,a critical problem first to be faced is what the typical characteristics of evolution of initial perturbations are in the short-range ensemble prediction system(EPS). Consequently,a short-range EPS based on the BGMmethod has been developed with WRF3. 6.The regular rescaling scheme has also been incorporated into this system.Meanwhile,the short-range EPS that has covered the uncertainties of horizontal wind,vertical velocity,potential temperature,geopotential height and water vapor mixture ratio takes the large-range rainstorm in southern China in June 2016 as an example to recognize the evolving mechanism of perturbations. The results showthat:(1) the perturbation growing process of physical quantities in the upper,middle and lower levels of model atmosphere can be divided into two stages,one of which is the rapid linear growth of perturbations and the perturbations quickly complete the total increase of themselves in this phase,another of which is the nonlinear stable phase of perturbations growing and the transition from the fast linearly growing phase to the nonlinear stable phase takes about 24 h.(2) The perturbations of physical quantities take approximately the same length of time to enter the nonlinear stable phase through the temporal evolution features of perturbation growth rate,correlation coefficient and perturbation growing modes.Nonetheless,when the perturbations come into the nonlinear stable stage,the numerical values and evolving characteristics of each assessment parameter are different for the same pressure level with different physical quantities or the same physical quantity at different pressure levels.Moreover,there is a diurnal oscillation phenomenon with time for each assessment parameter at the nonlinear stable stage.(3) For the initial ensemble of different random patterns with the same size of perturbation amplitude,the impacts of different random patterns on perturbations breeding mainly yield differences in the nonlinear stable stage while the differences between each pattern are too small to distinguish in the fast linearly growing stage.(4) For the initial ensemble of the same random pattern but with different sizes of perturbation amplitudes,the influences of different amplitudes on the evolution of perturbations mainly occurs in the fast linear growth phase,while the differences between each amplitude are quite small in the nonlinear stable phase.Additionally,the different sizes of initial amplitudes have no influence on the characteristic time scale of the perturbations getting into the nonlinear stable phase.
引文
Arribas A,2005.Test of a poor man's ensemble prediction system for short-range probability forecasting[J].Mon Wea Rev,133(7):1825-1839.
Bishop C H,Etherton B J,Majumdar S J,2001.Adaptive sampling with the ensemble transform Kalman filter.Part I:theoretical aspects[J].Mon Wea Rev,129(3):420-436.
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Bowler N E,Arribas A,Beare S E,2009.The local ETKF and SKEB:upgrades to the MOGREPS short-range ensemble prediction system[J].Quart JRoy Meteor Soc,135(640):767-776.
Buizza R,Palmer T N,1995.The singular vector structure of the atmospheric global circulation[J].J Atmos Sci,52(9):1434-1456.
陈超辉,李湘,何宏让,2018.基于对流尺度集合预报特性的局地增长模培育算法[J].中国科学:地球科学,48(4):510-520.Chen C H,Li X,He H R,2018.Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting[J].Science China Earth Science,61(4):462-472.(in Chinese).
陈静,薛纪善,颜宏,2005.一种新型的中尺度暴雨集合预报初值扰动方法研究[J].大气科学,29(5):717-726.Chen J,Xue J S,Yan H,2005.Anewinitial perturbation method for ensemble mesoscale heavy rain prediction[J].Chin J Atmos Sci,29(5):717-726.(in Chinese).
Du J,Tracton MS,2001.Implementation of a real-time short-range ensemble forecasting system at NCEP:an update[C]//9th Conference on Mesoscale Processes.Florida:Amer Meteor Soc:355-356.
Du J,Di Mego G,Tracton MS,2003.NCEP short-range ensemble forecasting(SREF)system:multi-IC,multi-model and multi-physics approach,research activities in atmospheric and oceanic modeling[R]//CAS/JSC Working Group on Numerical Experimentation(WGNE).
关吉平,张立凤,2009.增长模繁殖法在华南暴雨中期集合预报中的应用[J].热带气象学报,25(2):246-250.Guan J P,Zhang L F,2009.Application of the method of BGMin medium-range ensemble forecast for south China rainstorm[J].J Trop Meteor,25(2):246-250.(in Chinese).
Harlim J,2006.Errors in the initial conditions for numerical weather prediction:a study of error growth Patterns and error reduction with ensemble Filtering[D].Washington:University of Maryland.
Hoffman R N,Kalnay E,1983.Lagged average forecasting,an alternative to Monte Carlo forecasting[J].Tellus,35A(2):100-118.
Houtekamer P L,Mitchell H L,1998.Data assimilation using an ensemble Kalman filter technique[J].Mon Wea Rev,126(3):796-811.
Houtekamer P L,Lefaivre L,Derome J,et al.,1996.A system simulation approach to ensemble prediction[J].Mon Wea Rev,124(6):1225-1242.
Kay J K,Kim H M,2014.Characteristics of initial perturbations in the ensemble prediction system of the Korea Meteorological Administration[J].Wea Forecasting,29(3):563-581.
Leith C E,1974.Theoretical skill of Monte Carlo Forecasts[J].Mon Wea Rev,102(6):409-418.
李俊,杜钧,刘羽,2015.北京“7.21”特大暴雨不同集合预报方案的对比试验[J].气象学报,73(1):50-71.Li J,Du J,Liu Y,2015.A comparison of initial condition-,multi-physics and stochastic physics-based ensembles in predicting Beijing“7.21”excessive storm rain event[J].Acta Meteor Sinica,73(1):50-71.(in Chinese).
Molteni F,Buizza R,Palmer T N,et al.,1996.The ECMWF ensemble prediction system:methodology and validation[J].Quart J Roy Meteor Soc,122(529):73-119.
Schwartz C S,Romine G S,Smith K R,et al.,2014.Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter[J].Wea Forecasting,29(6):1295-1318.
Schwartz C S,Romine G S,Weisman ML,et al.,2015a.A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses[J].Wea Forecasting,30(5):1158-1181.
Schwartz C S,Romine G S,Sobash R A,2015b.NCAR's experimental real-time convection-allowing ensemble prediction system[J].Wea Forecasting,30(6):1645-1654.
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.
Wang X,Bishop C H,2003.A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes[J].J Atmos Sci,60(9):1140-1158.
Wei M,Toth Z,Wobus R,et al.,2006.Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP[J].Tellus A,58(1):28-44.
Wei M,Toth Z,Wobus R,et al.,2008.Initial perturbations based on ensemble transform(ET)technique in the NCEP global operational forecast system[J].Tellus A,60(1):62-79.
于永锋,张立凤,2005.基于增长模繁殖法的集合预报初始扰动饱和分析[J].大气科学,29(6):955-964.Yu Y F,Zhang L F,2005.A study of initial perturbation saturation in ensemble prediction based on the breeding of growing modes method[J].Chin J Atmos Sci,29(6):955-964.(in Chinese).
张涵斌,陈静,智协飞,等,2014.基于GRAPES_Meso的集合预报扰动方案设计与比较[J].大气科学学报,37(3):276-284.Zhang H B,Chen J,Zhi X F,et al.,2014.Design and comparison of perturbation schemes for GRAPES_Meso based ensemble forecast[J].Trans Atmos Sci,37(3):276-284.(in Chinese).
智协飞,孙晶,周文友,2015.2009年夏季西太平洋台风的集合预报和多模式集成预报试验[J].大气科学学报,38(5):633-640.Zhi X F,Sun J,Zhou W Y,2015.Ensemble and multi-model ensemble forecast of western Pacific typhoons during summer 2009[J].Trans Atmos Sci,38(5):633-640.(in Chinese).
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 X R,Min J Z,Cai Y C,et al.,2016.Convective-scale ensemble prediction experiments under different large-scale forcing with consideration of uncertainties in initial and lateral boundary condition[J].Acta Meteor Sinica,74(2):244-258.(in Chinese).
Bishop C H,Etherton B J,Majumdar S J,2001.Adaptive sampling with the ensemble transform Kalman filter.Part I:theoretical aspects[J].Mon Wea Rev,129(3):420-436.
Bishop C H,Reynolds C A,Tippett MK,2003.Optimization of the fixed global observing network in a simple model[J].J Atmos Sci,60(12):1471-1489.
Bowler N E,Mylne K R,2009.Ensemble transform Kalman filter perturbations for a regional ensemble prediction system[J].Quart J Roy Meteor Soc,135(640):757-766.
Bowler N E,Arribas A,Mylne K R,2008a.The MOGREPS short-range ensemble prediction system[J].Quart J Roy Meteorol Soc,134(632):703-722.
Bowler N E,Arribas A,Mylne K R,2008b.The benefits of multi-analysis and poor man's ensembles[J].Mon Wea Rev,136(11):4113-4129.
Bowler N E,Arribas A,Beare S E,2009.The local ETKF and SKEB:upgrades to the MOGREPS short-range ensemble prediction system[J].Quart JRoy Meteor Soc,135(640):767-776.
Buizza R,Palmer T N,1995.The singular vector structure of the atmospheric global circulation[J].J Atmos Sci,52(9):1434-1456.
陈超辉,李湘,何宏让,2018.基于对流尺度集合预报特性的局地增长模培育算法[J].中国科学:地球科学,48(4):510-520.Chen C H,Li X,He H R,2018.Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting[J].Science China Earth Science,61(4):462-472.(in Chinese).
陈静,薛纪善,颜宏,2005.一种新型的中尺度暴雨集合预报初值扰动方法研究[J].大气科学,29(5):717-726.Chen J,Xue J S,Yan H,2005.Anewinitial perturbation method for ensemble mesoscale heavy rain prediction[J].Chin J Atmos Sci,29(5):717-726.(in Chinese).
Du J,Tracton MS,2001.Implementation of a real-time short-range ensemble forecasting system at NCEP:an update[C]//9th Conference on Mesoscale Processes.Florida:Amer Meteor Soc:355-356.
Du J,Di Mego G,Tracton MS,2003.NCEP short-range ensemble forecasting(SREF)system:multi-IC,multi-model and multi-physics approach,research activities in atmospheric and oceanic modeling[R]//CAS/JSC Working Group on Numerical Experimentation(WGNE).
关吉平,张立凤,2009.增长模繁殖法在华南暴雨中期集合预报中的应用[J].热带气象学报,25(2):246-250.Guan J P,Zhang L F,2009.Application of the method of BGMin medium-range ensemble forecast for south China rainstorm[J].J Trop Meteor,25(2):246-250.(in Chinese).
Harlim J,2006.Errors in the initial conditions for numerical weather prediction:a study of error growth Patterns and error reduction with ensemble Filtering[D].Washington:University of Maryland.
Hoffman R N,Kalnay E,1983.Lagged average forecasting,an alternative to Monte Carlo forecasting[J].Tellus,35A(2):100-118.
Houtekamer P L,Mitchell H L,1998.Data assimilation using an ensemble Kalman filter technique[J].Mon Wea Rev,126(3):796-811.
Houtekamer P L,Lefaivre L,Derome J,et al.,1996.A system simulation approach to ensemble prediction[J].Mon Wea Rev,124(6):1225-1242.
Kay J K,Kim H M,2014.Characteristics of initial perturbations in the ensemble prediction system of the Korea Meteorological Administration[J].Wea Forecasting,29(3):563-581.
Leith C E,1974.Theoretical skill of Monte Carlo Forecasts[J].Mon Wea Rev,102(6):409-418.
李俊,杜钧,刘羽,2015.北京“7.21”特大暴雨不同集合预报方案的对比试验[J].气象学报,73(1):50-71.Li J,Du J,Liu Y,2015.A comparison of initial condition-,multi-physics and stochastic physics-based ensembles in predicting Beijing“7.21”excessive storm rain event[J].Acta Meteor Sinica,73(1):50-71.(in Chinese).
Molteni F,Buizza R,Palmer T N,et al.,1996.The ECMWF ensemble prediction system:methodology and validation[J].Quart J Roy Meteor Soc,122(529):73-119.
Schwartz C S,Romine G S,Smith K R,et al.,2014.Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter[J].Wea Forecasting,29(6):1295-1318.
Schwartz C S,Romine G S,Weisman ML,et al.,2015a.A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses[J].Wea Forecasting,30(5):1158-1181.
Schwartz C S,Romine G S,Sobash R A,2015b.NCAR's experimental real-time convection-allowing ensemble prediction system[J].Wea Forecasting,30(6):1645-1654.
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.
Wang X,Bishop C H,2003.A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes[J].J Atmos Sci,60(9):1140-1158.
Wei M,Toth Z,Wobus R,et al.,2006.Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP[J].Tellus A,58(1):28-44.
Wei M,Toth Z,Wobus R,et al.,2008.Initial perturbations based on ensemble transform(ET)technique in the NCEP global operational forecast system[J].Tellus A,60(1):62-79.
于永锋,张立凤,2005.基于增长模繁殖法的集合预报初始扰动饱和分析[J].大气科学,29(6):955-964.Yu Y F,Zhang L F,2005.A study of initial perturbation saturation in ensemble prediction based on the breeding of growing modes method[J].Chin J Atmos Sci,29(6):955-964.(in Chinese).
张涵斌,陈静,智协飞,等,2014.基于GRAPES_Meso的集合预报扰动方案设计与比较[J].大气科学学报,37(3):276-284.Zhang H B,Chen J,Zhi X F,et al.,2014.Design and comparison of perturbation schemes for GRAPES_Meso based ensemble forecast[J].Trans Atmos Sci,37(3):276-284.(in Chinese).
智协飞,孙晶,周文友,2015.2009年夏季西太平洋台风的集合预报和多模式集成预报试验[J].大气科学学报,38(5):633-640.Zhi X F,Sun J,Zhou W Y,2015.Ensemble and multi-model ensemble forecast of western Pacific typhoons during summer 2009[J].Trans Atmos Sci,38(5):633-640.(in Chinese).
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 X R,Min J Z,Cai Y C,et al.,2016.Convective-scale ensemble prediction experiments under different large-scale forcing with consideration of uncertainties in initial and lateral boundary condition[J].Acta Meteor Sinica,74(2):244-258.(in Chinese).