CWRF模式在中国夏季极端降水模拟的误差订正
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摘要
利用1980—2015年6—8月我国逐日降水观测数据评估CWRF模式(Climate-Weather Research and Forecasting model)多种参数化方案对我国夏季日降水的模拟能力,并考察累积概率变换偏差订正法(CDFt)的订正效果通过将广义帕累托分布(GPD)引入到偏差订正模型中,提出针对极端降水的累积概率变换偏差订正法(XCDFt),检验和评估其对极端降水订正的适用性。结果显示:CWRF模式微物理过程选用Morrison-aerosol参数化方案组合对我国降水场的模拟较好,CDFt订正效果良好;XCDFt偏差订正模型能够较好地提取模式建模与验证时期变化信号,订正后相比订正前与观测极端降水的概率分布更为接近;经过XCDFt订正后华南、华中和华北地区20年一遇的极端降水重现水平较模拟值更接近观测值,可为CWRF模式提高极端降水的业务预测水平提供参考。
Accurate forecast of extreme precipitation plays an important role in guiding the national economy and people's livelihood. The newly developed Climate-Weather Research and Forecasting model(CWRF) is applied to the operational forecasting experiment of China National Climate Center. It provides valuable scientific basis for improving the operational prediction for extreme precipitation.CWRF integrates a comprehensive ensemble of alternate parameterization schemes for each of key physical processes,including surface(land,ocean),planetary boundary layer,cumulus(deep,shallow),microphysics, cloud, aerosol, and radiation. This facilitates the use of an optimized physics ensemble approach to improve weather and climate prediction.Daily precipitation data simulated by CWRF from June to August during 1980-2015 and the observation by China Meteorological Administration are used to evaluate the performance of various parameterization schemes, and cumulative distribution function transform(CDFt) correction method is introduced.Based on the CDFt, the probability bias correction model XCDFt is proposed for extreme rainfall by introducing generalized Pareto distribution(GPD) and results are assessed. It shows that Morrison-aerosol parameterization scheme of CWRF model can simulate the spatial distribution of extreme precipitation better as well as correct daily precipitation by CDFt.Simulated results of Morrison-aerosol for daily precipitation threshold and super-threshold samples in summer in North China, Central China and East China are similar to those observed in this scheme. In Changsha, Jinan, Nanjing, and Nanning, GPD characterizes the distribution of each extreme precipitation well. It shows that XCDFt can preserve the CDF form of the observed calibrated precipitation and acquire the small change from the calibration to validations. XCDFt can improve the consistency between model simulation and observation in regional extreme precipitation recurrence levels. In North China, Central China and South China, after model simulation correction by XCDFt, the 20-year recurrence interval of extreme precipitation are closer to the observation, which shows that the revised data are more reliable.Error correction can only be used as a supplementary means to improve extreme precipitation prediction, though. The precision description of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction level.
Accurate forecast of extreme precipitation plays an important role in guiding the national economy and people's livelihood. The newly developed Climate-Weather Research and Forecasting model(CWRF) is applied to the operational forecasting experiment of China National Climate Center. It provides valuable scientific basis for improving the operational prediction for extreme precipitation.CWRF integrates a comprehensive ensemble of alternate parameterization schemes for each of key physical processes,including surface(land,ocean),planetary boundary layer,cumulus(deep,shallow),microphysics, cloud, aerosol, and radiation. This facilitates the use of an optimized physics ensemble approach to improve weather and climate prediction.Daily precipitation data simulated by CWRF from June to August during 1980-2015 and the observation by China Meteorological Administration are used to evaluate the performance of various parameterization schemes, and cumulative distribution function transform(CDFt) correction method is introduced.Based on the CDFt, the probability bias correction model XCDFt is proposed for extreme rainfall by introducing generalized Pareto distribution(GPD) and results are assessed. It shows that Morrison-aerosol parameterization scheme of CWRF model can simulate the spatial distribution of extreme precipitation better as well as correct daily precipitation by CDFt.Simulated results of Morrison-aerosol for daily precipitation threshold and super-threshold samples in summer in North China, Central China and East China are similar to those observed in this scheme. In Changsha, Jinan, Nanjing, and Nanning, GPD characterizes the distribution of each extreme precipitation well. It shows that XCDFt can preserve the CDF form of the observed calibrated precipitation and acquire the small change from the calibration to validations. XCDFt can improve the consistency between model simulation and observation in regional extreme precipitation recurrence levels. In North China, Central China and South China, after model simulation correction by XCDFt, the 20-year recurrence interval of extreme precipitation are closer to the observation, which shows that the revised data are more reliable.Error correction can only be used as a supplementary means to improve extreme precipitation prediction, though. The precision description of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction level.
引文
[1]王静,余锦华,何俊琦.江淮地区极端降水特征及其变化趋势的研究.气候与环境研究,2015,20(1):80-88.
[2]谭桂容,范艺媛,牛若芸.江淮地区强降水分型及其环流演变.应用气象学报,2018,29(4):396-409.
[3]翟盘茂,王萃萃,李威.极端降水事件变化的观测研究.气候变化研究进展,2007,3(3):144-148.
[4]霍治国,范雨娴,杨建莹,等.中国农业洪涝灾害研究进展.应用气象学报,2017,28(6):641-653.
[5]甘衍军,徐晶,赵平,等.暴雨致洪预报系统及其评估.应用气象学报,2017,28(4):385-398.
[6] Liang X Z,Sun C, Zheng X,et al. CWRF performance at downscaling China climate characteristics. Climate Dyn,2018,12:1-26.
[7]刘冠州,梁信忠.新一代区域气候模式(CWRF)国内应用进展.地球科学进展,2017,32(7):781-787.
[8] Liang X Z,Xu M, Yuan X,et al. Regional Climate-weather research and forecasting model(CWRF). Bull Amer Mateor Soc,2012,93(9):1363-1387.
[9]程娅蓓,任宏利,谭桂容.东亚夏季风模式跨季预测的EOF-相似误差订正.应用气象学报,2016,27(3):285-292.
[10]赵琳娜,刘莹,包红军,等.基于重组降水集合预报的洪水概率预报.应用气象学报,2017,28(5):544-554.
[11]吴启树,韩美,刘铭,等.基于评分最优化的模式降水预报订正算法对比.应用气象学报,2017,28(3):306-317.
[12]郝民,龚建东,田伟红,等.L波段探空仪湿度数据偏差订正及同化试验.应用气象学报,2018,29(5):559-570.
[13]卢新玉,魏鸣,王秀琴.TRMM月降水量产品在新疆地区的订正.应用气象学报,2017,28(3):379-384.
[14] Piani C,Haerter J O,soppola E. Statistical bias correction fordaily precipitation in regional climate models over Europe.Theor Appl Climatol, 2010,99(1-2):187-192.
[15]周莉,江志红.基于转移累积概率分布统计降尺度方法的未来降水预估研究:以湖南省为例.气象学报,2017,75(2):223-235.
[16] Amengual A,IIomar V,Romero R,et al. A statistical adjustment of regional climate model outputs to local scales:Application to Platja de Palma,Spain. J Climate,2012,25(3):939-957.
[17]周林,潘婕,张镭,等.概率调整法在气候模式模拟降水量订正中的应用.应用气象学报,2014,25(3):302-311.
[18]童尧,高学杰,韩振宇,等.基于RegCM4模式的中国区域日尺度降水模拟误差订正.大气科学,2017,41(6):1156-1166.
[19] Liang X Z,Choi H I,Kunkel K E,et al. Surface boundary conditions for mesoscale regional climate models. Earth Interactions,2005,9(12):305-319.
[20] Liang X Z,Xu M,Gan W,et al. Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer(MODIS)data. Journal of Geophysical Research Atmospheres, 2005,110(D11):1341-1355.
[21] Khain A, Lang S,Lynn B,et al. Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble(GCE)model. Meteorology&Atmospheric Physics,2003,82(1):97-137.
[22] Qiao F, Liang X. Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans. Journal of Advances in Modeling Earth Systems,2016,8(1-2):1-23.
[23] Qiao F, Liang X Z. Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States. Climate Dyn,2017,49(1-2):1-23.
[24] Bretherton C S,Park S S. A new moist turbulence parameterization in the Community Atmosphere Model. J Climate,2009,22(12):3422-3448.
[25] Kain J S. The Kain-Fritsch convective parameterization:An update. J Appl Meteor,2004,43:170-181.
[26] Zavisa I J. The step-mountain Eta coordinate model:Further developments of the convection,viscous sublayer,and turbu-lence closure schemes. Mon Wea Rev,1994,122(5):927-945.
[27] Grell G A,Devenyi D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett,2002,29(6):587-590.
[28] Han J,Pan II L. Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea Forecasting,2011,20:520-533.
[29] Donner L J. A cumulus parameterization including mass fluxes,convective vertical velocities,and mesoscale effects:Thermodynamic and hydrological aspects in a general circulation model. J Climate,2001(14):3444-3463.
[30] Emanuel K A, Ivkovirothman M. Development and evaluation of a convection scheme for use in climate models. J Atmos Sci,1999,56(11):1766-1782.
[31] Lin Y L, Farley R D, Orville II D. Bulk Parameterization of the snow field in a cloud model. J Appl Meteor,1983,22(6):1065-1092.
[32] Hong S Y,Lim J O J. The WRF single-moment 6-class microphysics scheme(WSM6). Journal of Korean Medical Science,2006,42:129-151.
[33] Thompson G,Rasmussen R M,Manning K. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I:Description and Sensitivity Analysis. Mon Wea Rev,2004,136(12):5095-5115.
[34] Thompson G, Eidhammer T. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci,2014,71(10):3636-3658.
[35] Morrison II, Thompson G, Tatarskii V. Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall Line:Comparison of one-and twomoment schemes. Mon Wea Rev, 2009,137(3):991-1007.
[36] Michelangeli P A, Vrac M,Loukos II. Probabilistic downscaling approaches:Application to wind cumulative distribution function. Geophys Res Lett,2009,36(11):163-182.
[37] Kallachc M,Vrac M,Navcau P,ct al. Nonstationary probabilistic downscaling of extreme precipitation. J Geophys Res,2011,116(D5),DOI:10. 1029/2010JD014892.
[2]谭桂容,范艺媛,牛若芸.江淮地区强降水分型及其环流演变.应用气象学报,2018,29(4):396-409.
[3]翟盘茂,王萃萃,李威.极端降水事件变化的观测研究.气候变化研究进展,2007,3(3):144-148.
[4]霍治国,范雨娴,杨建莹,等.中国农业洪涝灾害研究进展.应用气象学报,2017,28(6):641-653.
[5]甘衍军,徐晶,赵平,等.暴雨致洪预报系统及其评估.应用气象学报,2017,28(4):385-398.
[6] Liang X Z,Sun C, Zheng X,et al. CWRF performance at downscaling China climate characteristics. Climate Dyn,2018,12:1-26.
[7]刘冠州,梁信忠.新一代区域气候模式(CWRF)国内应用进展.地球科学进展,2017,32(7):781-787.
[8] Liang X Z,Xu M, Yuan X,et al. Regional Climate-weather research and forecasting model(CWRF). Bull Amer Mateor Soc,2012,93(9):1363-1387.
[9]程娅蓓,任宏利,谭桂容.东亚夏季风模式跨季预测的EOF-相似误差订正.应用气象学报,2016,27(3):285-292.
[10]赵琳娜,刘莹,包红军,等.基于重组降水集合预报的洪水概率预报.应用气象学报,2017,28(5):544-554.
[11]吴启树,韩美,刘铭,等.基于评分最优化的模式降水预报订正算法对比.应用气象学报,2017,28(3):306-317.
[12]郝民,龚建东,田伟红,等.L波段探空仪湿度数据偏差订正及同化试验.应用气象学报,2018,29(5):559-570.
[13]卢新玉,魏鸣,王秀琴.TRMM月降水量产品在新疆地区的订正.应用气象学报,2017,28(3):379-384.
[14] Piani C,Haerter J O,soppola E. Statistical bias correction fordaily precipitation in regional climate models over Europe.Theor Appl Climatol, 2010,99(1-2):187-192.
[15]周莉,江志红.基于转移累积概率分布统计降尺度方法的未来降水预估研究:以湖南省为例.气象学报,2017,75(2):223-235.
[16] Amengual A,IIomar V,Romero R,et al. A statistical adjustment of regional climate model outputs to local scales:Application to Platja de Palma,Spain. J Climate,2012,25(3):939-957.
[17]周林,潘婕,张镭,等.概率调整法在气候模式模拟降水量订正中的应用.应用气象学报,2014,25(3):302-311.
[18]童尧,高学杰,韩振宇,等.基于RegCM4模式的中国区域日尺度降水模拟误差订正.大气科学,2017,41(6):1156-1166.
[19] Liang X Z,Choi H I,Kunkel K E,et al. Surface boundary conditions for mesoscale regional climate models. Earth Interactions,2005,9(12):305-319.
[20] Liang X Z,Xu M,Gan W,et al. Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer(MODIS)data. Journal of Geophysical Research Atmospheres, 2005,110(D11):1341-1355.
[21] Khain A, Lang S,Lynn B,et al. Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble(GCE)model. Meteorology&Atmospheric Physics,2003,82(1):97-137.
[22] Qiao F, Liang X. Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans. Journal of Advances in Modeling Earth Systems,2016,8(1-2):1-23.
[23] Qiao F, Liang X Z. Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States. Climate Dyn,2017,49(1-2):1-23.
[24] Bretherton C S,Park S S. A new moist turbulence parameterization in the Community Atmosphere Model. J Climate,2009,22(12):3422-3448.
[25] Kain J S. The Kain-Fritsch convective parameterization:An update. J Appl Meteor,2004,43:170-181.
[26] Zavisa I J. The step-mountain Eta coordinate model:Further developments of the convection,viscous sublayer,and turbu-lence closure schemes. Mon Wea Rev,1994,122(5):927-945.
[27] Grell G A,Devenyi D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett,2002,29(6):587-590.
[28] Han J,Pan II L. Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea Forecasting,2011,20:520-533.
[29] Donner L J. A cumulus parameterization including mass fluxes,convective vertical velocities,and mesoscale effects:Thermodynamic and hydrological aspects in a general circulation model. J Climate,2001(14):3444-3463.
[30] Emanuel K A, Ivkovirothman M. Development and evaluation of a convection scheme for use in climate models. J Atmos Sci,1999,56(11):1766-1782.
[31] Lin Y L, Farley R D, Orville II D. Bulk Parameterization of the snow field in a cloud model. J Appl Meteor,1983,22(6):1065-1092.
[32] Hong S Y,Lim J O J. The WRF single-moment 6-class microphysics scheme(WSM6). Journal of Korean Medical Science,2006,42:129-151.
[33] Thompson G,Rasmussen R M,Manning K. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I:Description and Sensitivity Analysis. Mon Wea Rev,2004,136(12):5095-5115.
[34] Thompson G, Eidhammer T. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci,2014,71(10):3636-3658.
[35] Morrison II, Thompson G, Tatarskii V. Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall Line:Comparison of one-and twomoment schemes. Mon Wea Rev, 2009,137(3):991-1007.
[36] Michelangeli P A, Vrac M,Loukos II. Probabilistic downscaling approaches:Application to wind cumulative distribution function. Geophys Res Lett,2009,36(11):163-182.
[37] Kallachc M,Vrac M,Navcau P,ct al. Nonstationary probabilistic downscaling of extreme precipitation. J Geophys Res,2011,116(D5),DOI:10. 1029/2010JD014892.