不同模式扰动方案在风暴尺度集合预报中的对比试验研究
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摘要
由于适用于中长期集合预报的模式扰动技术在风暴尺度集合预报系统中的影响并不明确,为探究不同模式扰动方案在风暴尺度集合预报中的效果,基于WRF模式设计了3组模式扰动方案:多物理扰动(MP)方案、随机物理倾向扰动(Stochastically Perturbed Parameterization Tendencies,SPPT)方案以及由MP方案与SPPT方案组合构建的一种新混合扰动(SPMP)方案。对2013年7月5—6日发生在江淮流域的一次强对流天气过程进行了数值模拟。结果表明:MP方案在积分前期的降水概率评分较高,对高层大气的扰动效果更为合理;SPPT方案主要作用于积分中后期,对大气低层及近地面的扰动效果最为理想,尤其是对于地面水汽场的模拟;SPMP方案能显著提高大气中高层各预报变量的离散度,降低均方根误差,提升集合成员的可信度,有效弥补降水预报评分在单独使用MP方案和SPPT方案不同积分时段的不足。在扰动水平传播方向上,SPMP方案的扰动形态主要受MP方案主导;垂直方向上,SPMP方案在低层的扰动形态与SPPT方案一致,在高层受MP方案控制。波谱能量分析表明3组方案的扰动能量随积分时间均有向大尺度传播的趋势,SPMP方案能有效补偿两种方案能量在各尺度的耗散。
The impact of model perturbation technique that is applied to medium and long-term ensemble forecast is not clear when applied to the storm-scale ensemble forecasting system.In order to explore the possible impacts of this technique on storm-scale ensemble forecasting,three perturbation schemes are designed to study a severe convection weather process in Changjiang-Huaihe River basin from5-6 July 2013 using the Weather Research and Forecasting(WRF)model.These perturbation schemes include the multi-physics(MP)scheme,the Stochastically Perturbed Parameterization Tendencies(SPPT)scheme and the SPMP scheme.The SPMP scheme is a mixing of MP and SPPT.Results show the precipitation probability score of the MP scheme is better than that of the SPPT in the early period of integration and the effect of MP scheme in the upper atmosphere disturbance is more reasonable.The SPPT scheme mainly affects simulation in the middle and later periods of integration and performs well in the lower atmosphere and over the land,especially for the simulation of surface water vapor field.The SPMP can significantly improve the spread of forecast variables and reduce the root mean square error in the upper troposphere,and thus enhances the credibility of the ensemble members.The use of SPMP scheme can effectively compensate for the shortcomings that occur when only using the MP or SPPT scheme in different integration periods.The propagation of perturbation stream function in the SPMP is mainly dominated by MP in the horizontal direction.In the vertical direction,the pattern of SPMP in the lower layer is consistent with that of SPPT,but it is controlled by MP in the upper layer.The analysis of spectral energy shows the energy has a tendency to propagate from small scale to large scale with increasing integration time.And SPMP can effectively compensate for the energy dissipation shown in the other two schemes in different scales.
The impact of model perturbation technique that is applied to medium and long-term ensemble forecast is not clear when applied to the storm-scale ensemble forecasting system.In order to explore the possible impacts of this technique on storm-scale ensemble forecasting,three perturbation schemes are designed to study a severe convection weather process in Changjiang-Huaihe River basin from5-6 July 2013 using the Weather Research and Forecasting(WRF)model.These perturbation schemes include the multi-physics(MP)scheme,the Stochastically Perturbed Parameterization Tendencies(SPPT)scheme and the SPMP scheme.The SPMP scheme is a mixing of MP and SPPT.Results show the precipitation probability score of the MP scheme is better than that of the SPPT in the early period of integration and the effect of MP scheme in the upper atmosphere disturbance is more reasonable.The SPPT scheme mainly affects simulation in the middle and later periods of integration and performs well in the lower atmosphere and over the land,especially for the simulation of surface water vapor field.The SPMP can significantly improve the spread of forecast variables and reduce the root mean square error in the upper troposphere,and thus enhances the credibility of the ensemble members.The use of SPMP scheme can effectively compensate for the shortcomings that occur when only using the MP or SPPT scheme in different integration periods.The propagation of perturbation stream function in the SPMP is mainly dominated by MP in the horizontal direction.In the vertical direction,the pattern of SPMP in the lower layer is consistent with that of SPPT,but it is controlled by MP in the upper layer.The analysis of spectral energy shows the energy has a tendency to propagate from small scale to large scale with increasing integration time.And SPMP can effectively compensate for the energy dissipation shown in the other two schemes in different scales.
引文
蔡沅晨,闵锦忠,庄潇然.2017.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.高原气象,36(2):407-423.Cai YC,Min J Z,Zhuang X R.2017.Comparison of different stochastic physics perturbation schemes on a storm-scale ensemble forecast in a heavy rain event.Plateau Meteor,36(2):407-423(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.大气科学,29(5):717-726.Chen J,Xue J S,Yan H.2005.A new initial perturbation method of ensemble mesoscale heavy rain prediction.Chinese J Atmos Sci,29(5):717-726(in Chinese)
杜钧,李俊.2014.集合预报方法在暴雨研究和预报中的应用.气象科技进展,4(5):6-20.Du J,Li J.2014.Application of ensemble methodology to heavy-rain research and prediction.Adv Meteor Sci Technol,4(5):6-20(in Chinese)
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.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.Acta Meteor Sinica,73(1):50-71(in Chinese)
沈艳,潘旸,宇婧婧等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.Shen Y,Pan Y,Yu JJ,et al.2013.Quality assessment of hourly merged precipitation product over China.Tran Atmos Sci,36(1):37-46(in Chinese)
孙继松,陶祖钰.2012.强对流天气分析与预报中的若干基本问题.气象,38(2):164-173.Sun J S,Tao Z Y.2012.Some essential issues connected with severe convective weather analysis and forecast.Meteor Mon,38(2):164-173(in Chinese)
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.气象,39(5):543-555.Tan N,Chen J,Tian H.2013.Comparison between two global model stochastic perturbation schemes and analysis of perturbation propagation.Meteor Mon,39(5):543-555(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.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.Trans Atmos Sci,37(3):276-284(in Chinese)
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.高原气象,33(5):1323-1331.Zhang M,Min J Z,Qi Y C,et al.2014.Ensemble experiments research based on mass-fluxed cumulus convective parameterization of KF-ETA.Plateau Meteor,33(5):1323-1331(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.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.Acta Meteor Sinica,74(2):244-258(in Chinese)
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 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.Mon Wea Rev,139(6):1972-1995
Berner J,Fossell K R,Ha S Y,et al.2015.Increasing the skill of probabilistic forecasts:Understanding performance improvements from model-error representations.Mon Wea Rev,143(4):1295-1320
Bouttier F,ViéB,Nuissier O,et al.2014.Impact of stochastic physics in a convection-permitting ensemble.Mon Wea Rev,140(11):3706-3721
Buizza R,Hollingsworth A,Lalaurette F,et al.1999.Probabilistic predictions of precipitation using the ECMWF ensemble prediction system.Wea Forecasting,14(2):168-189
Candille G.2009.The multiensemble approach:The NAEFS example.Mon Wea Rev,137(5):1655-1665
Charron M,Pellerin G,Spacek L,et al.2010.Toward random sampling of model error in the Canadian ensemble prediction system.Mon Wea Rev,138(5):1877-1901
Christensen H M,Moroz I M,Palmer T N.2015.Stochastic and perturbed parameter representations of model uncertainty in convection parameterization.J Atmos Sci,72(6):2525-2544
Duda J D,Wang X G,Kong F Y,et al.2016.Impact of a stochastic kinetic energy backscatter scheme on warm season convectionallowing ensemble forecasts.Mon Wea Rev,144(5):1887-1908
Hacker J P,Ha S Y,Snyder C,et al.2011.The U.S.air force weather agency's mesoscale ensemble:Scientific description and performance results.Tellus A,63(3):625-641
Hamill T M.2000.Interpretation of rank histograms for verifying ensemble forecasts.Mon Wea Rev,129(3):550-560
Houtekamer P L,Lefaivre L,Derome J,et al.1996.A system simulation approach to ensemble prediction.Mon Wea Rev,124(6):1225-1242
Johnson A,Wang X G,Xue M,et al.2011.Hierarchical cluster analysis of a convection-allowing ensemble during the hazardous weather testbed 2009 spring experiment.PartⅡ:Ensemble clustering over the whole experiment period.Mon Wea Rev,139(12):3694-3710
Kong F Y,Droegemeier K K,Hickmon N L.2006.Multiresolution ensemble forecasts of an observed tornadic thunderstorm system.PartⅠ:Comparsion of coarse-and fine-grid experiments.Mon Wea Rev,134(3):807-833
Kong F Y,Droegemeier K K,Hickmon N L.2007.Multiresolution ensemble forecasts of an observed tornadic thunderstorm system.PartⅡ:Storm-scale experiments.Mon Wea Rev,135(3):759-782
Lilly D K.1990.Numerical prediction of thunderstorms---has its time come?.Quart J Roy Meteor Soc,116(494):779-798
Martin W J,Xue M.2006.Sensitivity analysis of convection of the24 May 2002 IHOP case using very large ensembles.Mon Wea Rev,134(1):192-207
Montani A,Cesari D,Marsigli C,et al.2011.Seven years of activity in the field of mesoscale ensemble forecasting by the COS-MO-LEPS system:Main achievements and open challenges.Tellus A,63(3):605-624
Palmer T N,Buizza R,Doblas-Reyes F,et al.2009.Stochastic parametrization and model uncertainty.ECMWF Technical Memorandum,42pp
Ray P S,Johnson B C,Johnson K W,et al.1981.The morphology of several tornadic storms on 20 May 1977.J Atmos Sci,38(8):1643-1663
Shutts G.2005.A kinetic energy backscatter algorithm for use in ensemble prediction systems.Quart J Roy Meteor Soc,131(612):3079-3102
Stensrud D J,Brooks H E,Du J,et al.1999.Using ensembles for short-range forecasting.Mon Wea Rev,127(4):433-446
Toth Z,Kalnay E.1997.Ensemble forecasting at NCEP and the breeding method.Mon Wea Rev,1997,125(12):3297-3319
Wang X G,Bishop C H.2003.A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes.JAtmos Sci,60(9):1140-1158
Wang X G,Bishop C H,Julier S J.2004.Which is better,an ensemble of positive-negative pairs or a centered spherical simplex ensemble.Mon Wea Rev,132(7):1590-1605
Wang Y,Bellus M,Geleyn J F,et al.2014.A new method for generating initial condition perturbations in a regional ensemble prediction system:Blending.Mon Wea Rev,142(5):2043-2059
Wei M Z,Toth Z,Wobus R,et al.2008.Initial perturbations based on the ensemble transform(ET)technique in the NCEPglobal operational forecast system.Tellus A,60(1):62-79
Xue M,Kong F,Thomas K W,et al.2010.CAPS realtime storm scale ensemble and high resolution forecasts for the NOAA hazardous weather testbed 2010 spring experiment[C]∥Proceedings of the 25th Conference on Severe Local Storms.Denver,CO:American Meteorological Society
Yussouf N,Stensrud D J.2012.Comparison of single-parameter and multiparameter ensembles for assimilation of radar observations using the ensemble Kalman Filter.Mon Wea Rev,140(2):562-586
Zhang F,Snyder C,Rotunno R.2003.Effects of moist convection on mesoscale predictability.J Atmos Sci,60(9):11731185
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.大气科学,29(5):717-726.Chen J,Xue J S,Yan H.2005.A new initial perturbation method of ensemble mesoscale heavy rain prediction.Chinese J Atmos Sci,29(5):717-726(in Chinese)
杜钧,李俊.2014.集合预报方法在暴雨研究和预报中的应用.气象科技进展,4(5):6-20.Du J,Li J.2014.Application of ensemble methodology to heavy-rain research and prediction.Adv Meteor Sci Technol,4(5):6-20(in Chinese)
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.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.Acta Meteor Sinica,73(1):50-71(in Chinese)
沈艳,潘旸,宇婧婧等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.Shen Y,Pan Y,Yu JJ,et al.2013.Quality assessment of hourly merged precipitation product over China.Tran Atmos Sci,36(1):37-46(in Chinese)
孙继松,陶祖钰.2012.强对流天气分析与预报中的若干基本问题.气象,38(2):164-173.Sun J S,Tao Z Y.2012.Some essential issues connected with severe convective weather analysis and forecast.Meteor Mon,38(2):164-173(in Chinese)
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.气象,39(5):543-555.Tan N,Chen J,Tian H.2013.Comparison between two global model stochastic perturbation schemes and analysis of perturbation propagation.Meteor Mon,39(5):543-555(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.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.Trans Atmos Sci,37(3):276-284(in Chinese)
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.高原气象,33(5):1323-1331.Zhang M,Min J Z,Qi Y C,et al.2014.Ensemble experiments research based on mass-fluxed cumulus convective parameterization of KF-ETA.Plateau Meteor,33(5):1323-1331(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.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.Acta Meteor Sinica,74(2):244-258(in Chinese)
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 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.Mon Wea Rev,139(6):1972-1995
Berner J,Fossell K R,Ha S Y,et al.2015.Increasing the skill of probabilistic forecasts:Understanding performance improvements from model-error representations.Mon Wea Rev,143(4):1295-1320
Bouttier F,ViéB,Nuissier O,et al.2014.Impact of stochastic physics in a convection-permitting ensemble.Mon Wea Rev,140(11):3706-3721
Buizza R,Hollingsworth A,Lalaurette F,et al.1999.Probabilistic predictions of precipitation using the ECMWF ensemble prediction system.Wea Forecasting,14(2):168-189
Candille G.2009.The multiensemble approach:The NAEFS example.Mon Wea Rev,137(5):1655-1665
Charron M,Pellerin G,Spacek L,et al.2010.Toward random sampling of model error in the Canadian ensemble prediction system.Mon Wea Rev,138(5):1877-1901
Christensen H M,Moroz I M,Palmer T N.2015.Stochastic and perturbed parameter representations of model uncertainty in convection parameterization.J Atmos Sci,72(6):2525-2544
Duda J D,Wang X G,Kong F Y,et al.2016.Impact of a stochastic kinetic energy backscatter scheme on warm season convectionallowing ensemble forecasts.Mon Wea Rev,144(5):1887-1908
Hacker J P,Ha S Y,Snyder C,et al.2011.The U.S.air force weather agency's mesoscale ensemble:Scientific description and performance results.Tellus A,63(3):625-641
Hamill T M.2000.Interpretation of rank histograms for verifying ensemble forecasts.Mon Wea Rev,129(3):550-560
Houtekamer P L,Lefaivre L,Derome J,et al.1996.A system simulation approach to ensemble prediction.Mon Wea Rev,124(6):1225-1242
Johnson A,Wang X G,Xue M,et al.2011.Hierarchical cluster analysis of a convection-allowing ensemble during the hazardous weather testbed 2009 spring experiment.PartⅡ:Ensemble clustering over the whole experiment period.Mon Wea Rev,139(12):3694-3710
Kong F Y,Droegemeier K K,Hickmon N L.2006.Multiresolution ensemble forecasts of an observed tornadic thunderstorm system.PartⅠ:Comparsion of coarse-and fine-grid experiments.Mon Wea Rev,134(3):807-833
Kong F Y,Droegemeier K K,Hickmon N L.2007.Multiresolution ensemble forecasts of an observed tornadic thunderstorm system.PartⅡ:Storm-scale experiments.Mon Wea Rev,135(3):759-782
Lilly D K.1990.Numerical prediction of thunderstorms---has its time come?.Quart J Roy Meteor Soc,116(494):779-798
Martin W J,Xue M.2006.Sensitivity analysis of convection of the24 May 2002 IHOP case using very large ensembles.Mon Wea Rev,134(1):192-207
Montani A,Cesari D,Marsigli C,et al.2011.Seven years of activity in the field of mesoscale ensemble forecasting by the COS-MO-LEPS system:Main achievements and open challenges.Tellus A,63(3):605-624
Palmer T N,Buizza R,Doblas-Reyes F,et al.2009.Stochastic parametrization and model uncertainty.ECMWF Technical Memorandum,42pp
Ray P S,Johnson B C,Johnson K W,et al.1981.The morphology of several tornadic storms on 20 May 1977.J Atmos Sci,38(8):1643-1663
Shutts G.2005.A kinetic energy backscatter algorithm for use in ensemble prediction systems.Quart J Roy Meteor Soc,131(612):3079-3102
Stensrud D J,Brooks H E,Du J,et al.1999.Using ensembles for short-range forecasting.Mon Wea Rev,127(4):433-446
Toth Z,Kalnay E.1997.Ensemble forecasting at NCEP and the breeding method.Mon Wea Rev,1997,125(12):3297-3319
Wang X G,Bishop C H.2003.A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes.JAtmos Sci,60(9):1140-1158
Wang X G,Bishop C H,Julier S J.2004.Which is better,an ensemble of positive-negative pairs or a centered spherical simplex ensemble.Mon Wea Rev,132(7):1590-1605
Wang Y,Bellus M,Geleyn J F,et al.2014.A new method for generating initial condition perturbations in a regional ensemble prediction system:Blending.Mon Wea Rev,142(5):2043-2059
Wei M Z,Toth Z,Wobus R,et al.2008.Initial perturbations based on the ensemble transform(ET)technique in the NCEPglobal operational forecast system.Tellus A,60(1):62-79
Xue M,Kong F,Thomas K W,et al.2010.CAPS realtime storm scale ensemble and high resolution forecasts for the NOAA hazardous weather testbed 2010 spring experiment[C]∥Proceedings of the 25th Conference on Severe Local Storms.Denver,CO:American Meteorological Society
Yussouf N,Stensrud D J.2012.Comparison of single-parameter and multiparameter ensembles for assimilation of radar observations using the ensemble Kalman Filter.Mon Wea Rev,140(2):562-586
Zhang F,Snyder C,Rotunno R.2003.Effects of moist convection on mesoscale predictability.J Atmos Sci,60(9):11731185