雷达资料三维变分同化研究
摘要
本文设计了一个雷达资料三维变分同化方案并采用梅雨和台风天气的实际雷达观测资料进行了同化和预报试验。文献中广泛应用的雷达径向风和反射率因子的观测算子被用来建立观测资料和模式变量之间的物理联系。较强的垂直运动是中尺度天气系统的典型特征之一,本文采用了两种技术来处理垂直速度。其一是采用Richardson方程通过水平风场、温度和气压来对垂直速度进行诊断,其二是利用统计方法由观测的反射率因子反演出垂直速度后,再采用Richardson方程作为观测算子对反演的垂直速度进行同化。研究发现,前一种方法得到的垂直速度偏小,分析出的中尺度对流系统不明显,后一种技术可以得到比较明显的三维中尺度环流结构。同时还发现下边界地形和Richardson方程中的加热项对垂直速度的同化有重要影响。水汽和云水采用一定的诊断方法得到,以构造动力、热力和水成物相互协调的三维对流结构。梅雨和台风暴雨个例试验结果表明,本文同化技术在缓解spinup现象、提高中尺度数值预报水平方面均显示出明显的正效果。
论文的创新之处和主要结论有如下几个方面:(1)本文建立的雷达资料三维变分中尺度同化系统是基本合理和有效的,单点检验证明同化系统中各观测算子和约束关系是合理的,天气个例试验显示同化雷达资料后预报结果均得到较大改善。(2)以Richardson方程作为观测算子同化垂直速度可以改善初始场中中尺度系统的三维动力和热力结构,方程中的非绝热项对同化结果有重要影响。(3)淮河暴雨试验结果表明同化雷达资料可以改善梅雨锋(切变线)上的中-β尺度对流系统的预报,缓解Spinup现象,并实现数值模式的热启动。(4)登陆台风试验结果表明同化雷达资料可以提高台风降水、强度和移动路径的预报水平,并改善初始场中台风螺旋雨带上的中-β尺度对流系统的结构。
鉴于中尺度和天气尺度天气系统之间的巨大差别,在中尺度资料三维变分同化时,背景误差相关模型的设置、观测误差结构和观测资料的代表性等方面还需要进行更加深入的研究。
A Doppler weather radar data assimilation scheme with three dimensional variational algorithms is proposed and tested with real observational radar data of both Meiyu torrential rain and typhoon events. The observational operators, commonly used in literatures, describing the physical connection between the fundamental observational elements, i.e. the radial velocity and the intensity of reflectivity, and the model state variables are introduced in this paper. Two approaches dealing with the vertical velocity, which is crucial for meso scale weather system and appears in the observational operators, are compared. One of them introduces the Richardson's equation to compute diagnostically the vertical velocity with horizontal wind, temperature, pressure and its tendency. The other one retrieves vertical velocity from the reflectivity using statistical algorithm and assimilates the vertical velocity taking Richardson's equation as observational operator. It is found that the vertical velocity derived by the former is usually too weak while the later gets the distinct three dimensional convective structure of meso-scale system with strong vertical velocity. It is also found that lower terrain and the diabatic term in the Richardson's equation should not be neglected in assimilating vertical velocity. Vapor and cloud are diagnosticated in order to perfect the convective system. The results of assimilation and prediction experiments with this scheme using operational radar data for a torrential rain event in the Meiyu season and a Typhoon case are analyzed in detail. Both cases show the positive contribution to relief Spin-up phenomenon and improvements of prediction of precipitation caused by meso-P scale convective systems.
The innovations and primary conclusions are summarized as follows.
(1) The mesoscale 3DVAR radar data assimilation system created in this paper is basically reasonable and effective, single observational datum experiment demonstrates the relations among variables are rational, and the weather cases experiments show assimilating radar data can improve prediction results.
(2) Taking Richardson's equation as observation factor to assimilate vertical velocity can advance the dynamical and thermal structure of mesoscale weather system, and the heating term of Richardson's equation has great impact on the results.
(3) The weather case experiment of Meiyu torrential precipitation indicates assimilating radar data can ameliorate the prediction of meso-P scale convective weather system on Meiyu front or shear line, relief Spinup phenomenon, and hot-start NWP model.
(4) Simulation of a typhoon case presents assimilating radar data advanced prediction of typhoon precipitation, intensity and track, enhanced the meso-P scale system on typhoon spiral rain-band.
Because of the fatal differs between large-scale and mesoscale weather systems, such problems as relevant model of background error, representativeness of observation data and observation error structure still need to carry on deeper research.
论文的创新之处和主要结论有如下几个方面:(1)本文建立的雷达资料三维变分中尺度同化系统是基本合理和有效的,单点检验证明同化系统中各观测算子和约束关系是合理的,天气个例试验显示同化雷达资料后预报结果均得到较大改善。(2)以Richardson方程作为观测算子同化垂直速度可以改善初始场中中尺度系统的三维动力和热力结构,方程中的非绝热项对同化结果有重要影响。(3)淮河暴雨试验结果表明同化雷达资料可以改善梅雨锋(切变线)上的中-β尺度对流系统的预报,缓解Spinup现象,并实现数值模式的热启动。(4)登陆台风试验结果表明同化雷达资料可以提高台风降水、强度和移动路径的预报水平,并改善初始场中台风螺旋雨带上的中-β尺度对流系统的结构。
鉴于中尺度和天气尺度天气系统之间的巨大差别,在中尺度资料三维变分同化时,背景误差相关模型的设置、观测误差结构和观测资料的代表性等方面还需要进行更加深入的研究。
A Doppler weather radar data assimilation scheme with three dimensional variational algorithms is proposed and tested with real observational radar data of both Meiyu torrential rain and typhoon events. The observational operators, commonly used in literatures, describing the physical connection between the fundamental observational elements, i.e. the radial velocity and the intensity of reflectivity, and the model state variables are introduced in this paper. Two approaches dealing with the vertical velocity, which is crucial for meso scale weather system and appears in the observational operators, are compared. One of them introduces the Richardson's equation to compute diagnostically the vertical velocity with horizontal wind, temperature, pressure and its tendency. The other one retrieves vertical velocity from the reflectivity using statistical algorithm and assimilates the vertical velocity taking Richardson's equation as observational operator. It is found that the vertical velocity derived by the former is usually too weak while the later gets the distinct three dimensional convective structure of meso-scale system with strong vertical velocity. It is also found that lower terrain and the diabatic term in the Richardson's equation should not be neglected in assimilating vertical velocity. Vapor and cloud are diagnosticated in order to perfect the convective system. The results of assimilation and prediction experiments with this scheme using operational radar data for a torrential rain event in the Meiyu season and a Typhoon case are analyzed in detail. Both cases show the positive contribution to relief Spin-up phenomenon and improvements of prediction of precipitation caused by meso-P scale convective systems.
The innovations and primary conclusions are summarized as follows.
(1) The mesoscale 3DVAR radar data assimilation system created in this paper is basically reasonable and effective, single observational datum experiment demonstrates the relations among variables are rational, and the weather cases experiments show assimilating radar data can improve prediction results.
(2) Taking Richardson's equation as observation factor to assimilate vertical velocity can advance the dynamical and thermal structure of mesoscale weather system, and the heating term of Richardson's equation has great impact on the results.
(3) The weather case experiment of Meiyu torrential precipitation indicates assimilating radar data can ameliorate the prediction of meso-P scale convective weather system on Meiyu front or shear line, relief Spinup phenomenon, and hot-start NWP model.
(4) Simulation of a typhoon case presents assimilating radar data advanced prediction of typhoon precipitation, intensity and track, enhanced the meso-P scale system on typhoon spiral rain-band.
Because of the fatal differs between large-scale and mesoscale weather systems, such problems as relevant model of background error, representativeness of observation data and observation error structure still need to carry on deeper research.
引文
[1]寿绍文等,中尺度气象学.气象出版社,北京,2003,370pp.
[2]张杰,中小尺度天气学.气象出版社,北京,2006,281pp.
[3]Lorenz,E.N.,The predictability of a flow which possesses many scales of motion.Tellus,1969,21,289-307.
[4]Browning,K.A.,The mesoscale data base and its use in mesoscale forecasting.Q.J.R.Met.Soc.,1989,115,717-62.
[5]陆汉城,杨国祥,中尺度天气原理和预报.气象出版社,北京,2004,301pp.
[6]Francois,B.and Ludovic,A.,The potential of data assimilation for nowcasting promises and limits of 3-D variational approach.World weather research program symposium on nowcasting and very short range forecasting,Toulouse,France,5-9th September 2005,P3.05.
[7]Schlatter,T.,Variational assimilation of meteorological observations in the lower atmosphere:atutorial on how it works.J.Atmos.Solar Terrestrial Phys.,2000,62,1057-1070.
[8]Talagrand,O.,Assimilation of Observations,an Introduction.J.Meteor.Soc.Japan.Special lssue 75(1B),1997,191-209.
[9]Richardson,Lewis F.,Weather Prediction by Numerical Process.Cambridge.Cambridge University Press.1922,236pp.Reprinted by Dover Publications,New York,with a new Introduction by Sydney Chapman.1965,236 pp.
[10]Charney,J.G.,Fjφtoft,R.and Von Neumann,J.,Numerical integration of the barotropic vorticity equation.Tellus,1950,2,237-254.
[11]Panofsky,H.A.,Objective weather map analysis.J.Meteor.,1949,6,386-392.
[12]Bergthorsson,P.and B.Doos,Numerical weather map analysis.Tellus,1955,7,329-340.
[13]Cressman,G.P.,An operational objective analysis system.Mon.Wea.Rev.,1959,87,367-374.
[14]Hoke,J.E.and R.A.Anthes,The initialization of numerical models by a dynamic-initialization technique.Mon.Wea.Rev.,1976,104(12),1551-1556.
[15]Kistler,R.E.,A study of data assimilation techniques in an autobarotropic primitive.equation channel model.MS Thesis,Dept of Meteorology,PennState University.1974,84pp.
[16]Eliassen,A.,Provisional report on calculation of spatial covariance and autocorrelation of the pressure field.Report 5,Inst.Weather and Climate Research,Acad.Sci.,Oslo.1954.
[17]Gandin,L.S.,Objective analysis of meteorological fields.Leningrad Hydromet.Press.1963,242pp.
[18]Lewis,J.M.and J.C.Derber,The Use of Adjoint Equations to Solve a Variational Adjustment Problem with Advective Constraints.Tellus,1985,37A,309-327.
[19]Le Dimet,F.X.and O.Talagrand,Variational Algorithms for Analysis and Assimilation of Meteorological Observations.Theoretical Aspects.Tellus,1986,38A,97-110.
[20]Talagrand,O.and P.Courtier,Variational assimilation of meteorological observations with the adjoint vorticity equation.Part 1:Theory.Q.J.R.Meteorol.Soc,' 1987,113,1311-1328.
[21]Courtier,P.and O.Talagrand,Variational assimilation of meteorological observations with the adjoint vorticity equation.Part 2:Numerical results.Q.J.R.Meteorol.Soc,1987,113,1329-1368.
[22]Kalman,R.E.,A new approach to linear filtering and prediction problems.Trans.ASME J.Basic Enging.,1960,82,35-45.
[23]Ghil,M.,Advances in sequential estimation for atmospheric and oceanic flows.J.Meteor.Soc.Japan,1997,75,289-304.
[24]Ide,K.,P.Courtier,M.Ghil,and A.Lorenc,Unified notation for data assimilation:Operational,sequential and variational.J.Meteor.Soc.Japan,1997,75,181-189.
[25]Houtekamer,P.L.,L.Lefaivre,J.Derome,H.Ritchie and H.L.Mitchell,A system simulation approach to ensemble prediction.Mon.Wea.Rev.,1996,124,1225-1242.
[26]Hamill,T.M.and C.Snyder,A hybrid ensemble Kalman filter/3D-variational analysis scheme.Mon.Wea.Rev.,2000,128,2905-2919.
[27]Kalnay,E.,Atmospheric modeling,data assimilation and predictability.Cambridge University Press,United Kingdom,2003,364pp.
[28]Krishnamurti,T.N.et al.,Physical initialization for numerical weather prediction over the tropics.Tellus,1991,43AB,53-81.
[29]Gregoric,G.,Diagnostics of deep convection using radar data and mesoscale numerical model.30th conference on Radar Meteorology,AMS,2001,177-178.
[30]Haase,G.,Crewell,S.,Simmer,C,Wergen,W.,Assimilation of radar data in mesoscale models:Physical initialization and latent heat nudging.Phys Chem Earth(B),2000,25,1237-1242.
[31]Albers,S.C,The LAPS wind analysis.Wea.and.Forecasting,1995,10,342-352.
[32]Xu,M.,N.A.Crook,Y.liu,and R.Rasmussen,Real-time analysis and short term forecasting of snowbands using a mesoscale model.11th Conf.on Aviation,Range and Aerospace Meteo.,Hyannis,MA,Amer.Meteo.Soc.2004.
[33]Jones,C.D.and B.Macpherson,A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model.Meteor.Appl,1997,4,269-277.
[34]Lin,Y.,M.E.Baldwin,K.E.Mitchell,E.Rogers,and G.J.DiMego,Spring 2001 changes to the NCEP Eta Analysis and Forecast System:Assimilation of observed precipitation.18th Conference on Weather Analysis and Forecasting and the 14th Conference on Numerical Weather Prediction,AMS,2001.J92-J95.
[35]Guo,Y.R.,Y.H.Kuo,J.Dudhia,D.Parsons,and C.Rocken,Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case.Mon.Wea.Rev.,2000,128,619-643.
[36]Zupanski,M.,Zupanski,D.,Parrish,D.F.,Rogers,E.and DiMego,G.J.,Four-dimensional variational data assimilation for the blizzard of 2000.Mon.Weather Rev.,2002,130,1967-1988.
[37]Marecal,V.and Mahfouf,J.F.,Experiments on 4D-Var assimilation of rainfall data using an incremental formulation.Q.J.R.Meteorol.Soc,2003,129,3137-3160.
[38]Koizumi,K.,Ishikawa,Y.and Tsuyuki,T.,Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts.SOLA,2005,1,45-48.
[39]Sun,J.and N.A.Crook,Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part Ⅰ.Model development and simulated data experiments.J.Atmos.Sci.,1997,54,1642-1661.
[40]Sun,J.and N.A.Crook,Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part Ⅱ.Retrieval experiments of an observed Florida convective storm.J.Atmos.Sci.,1998,55,835-852.
[41]Sun,J.and N.A.Crook,Real-time low-level wind and temperature analysis using single WSR-88D data.Weather Forecasting,2001,16,117-132.
[42]Crook,N.A.and Sun J.,Assimilating radar,surface and profiler data for the Sydney 2000 forecast demonstration project.J.Atmos.Oceanic Technol,2002,19,888-898.
[43]Sun,J.,Initialization and numerical forecasting of a supercell storm observed during STEPS.Mon.Weather Rev.,2005,133,793-164.
[44]Wu,B.,J.Verlinde,and J.Sun,Dynamical and microphysical retrievals from Doppler radar observations of a deep convective cloud.J.Atmos.Sci.,2000,57,262-283.
[45]Xiao,Q.,Y.H.Kuo,J.Sun,W.C.Lee,D.M.Barker,and E.Lim,An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa(2002)at landfall.J.Appl.Meteor.Climatol,2007,46,14-22.
[46]Xiao,Q.,and J.Sun,Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002.Mon.Wea.Rev.,2007,135,3381-3404.
[47]Snyder,C.and Zhang,F.,Assimilation of simulated Doppler radar observations with an ensemble Kalman filter.Mon.Weather Rev.,2003,131,1663-1677.
[48]Zhang,F.,Snyder,C.and Sun,J.,Impacts of initial estimate and observations on convective-scale data assimilations.Mon.Weather Rev.,2004,132,1238-1253.
[49]Caya,A.,Sun J.and Snyder,C,A comparison between the 4D-Var and the ensemble Kalman filter techniques for radar data assimilation.Mon.Weather Rev.,2005,133,3081-3094.
[50]Lhermitte,R.M.,and D.Atlas,Precipitation motion by pulse Doppler.Preprints Ninth Weather Radar Conf.,Amer.Meteor.Soc,1961,218-223.
[51]Browning,K.A.,and R.Wexler,The determination of kinematic properties of a wind field using Doppler radar.J.Applied.Meteor.,1968,7,105-113.
[52]Srivastava,R.C,T.J.Matejka and T.J.Lorello.,Doppler radar study of the trailing anvil region associated with a squall line.Journal of the Atmospheric Sciences,1986,43,356-377.
[53]Matejka,T.,and R.C.Srivastava.,An improved version of the extended velocity-azimuth display analysis of Single-doppler radar data.Journal of Atmospheric and Oceanic Technology,1991,8,453-466.
[54]Waldteufel,P.and H.Corbin,On the analysis of single-Doppler radar data,J.Appl.Meteor.,1979,18,532-542.
[55]McGinley,J.A.,The local analysis and prediction system.Pre-prints,12th Conf.On Weather Analysis and Forecasting,Monterey,CA,Amer.Meteor.Soc,1989,15-20.
[56]Brewster,K.,Implementation of a Bratseth analysis scheme including Doppler radar.15th conference on Weather Analysis and Forecasting,1996,92-95.
[57]Benjamin,S.G.,K.A.Brewster,R.Brummer,B.F.Jewett,T.W.Schlatter,T.L.Smith,and P.A.Stamus,An isentropic meso-scale analysis system and its sensitivity to aircraft and surface observations.Mon.Wea.Rev.,1991,117,1586-1603.
[58]Liou,Y.C.,Retrieval of three-dimensional wind and temperature fields from one component wind data by using the four-dimensional data assimilation technique.Master's thesis,University of Oklahoma,1990,112pp.
[59]Charney,J.,M.Halem,and R.Jastrow,Use of incomplete historical data to infer the present state of the atmosphere.J.Atmos.Sci.,1969,26,1160-1163.
[60]Gal-Chen,T.,A method for the initialization of the anelastic equations:Implications for matching models with observations.Mon.Wea.Rev.,1978,106,587-602.
[61]Parrish,D.F.and J.Purser,Anisotropic covariances in 3D-Var:Application to hurricane Doppler radar observations.HIRLAM 4 workshop on Variational analysis in limited area models,Meteo-France,Toulouse.1998.
[62]Lindskog,M.,H.Jarvinen,and D.B.Michelson,Assimilation of radar radial wind in the HIRLAM 3D-Vat.Phys.Chem.Earth(B),2000,25,1243-1250.
[63]Lindskog,M.,K.Salonen,H.Jarvinen,and D.B.Michelson,Doppler radar wind data Assimilation with HIRLAM 3DVAR.Mon.Wea.Rev.,2004,132,1081-1092.
[64]Lindskog M.,H.Jarvinen,and D.B.Michelson,Development of Doppler radar wind data assimilation for the HIRLAM 3D-Vat.HIRLAM Technical Report 52,HIRLAM-5 Project.2002.
[65]Xiao,Q.,Y.H.Kuo,J.Sun,W.C.Lee,E.Lira,Y.R.Guo,and D.M.Barker,Assimilation of Doppler radar observations with a regional 3D-Var system:Impact of Doppler velocities on forecasts of a heavy rainfall case.J.Appl.Meteor.,2005,44,768-788.
[66]Wolfsberg,D.,Retrieval of three-dimensional wind and temperature fields from single-Doppler radar data.CIMMS Report No.84,Cooperative Institute for Mesoscale Meteorological Studies,401 East Boyd,100 East Boyd St.,OK,USA.1987.
[67]Sun,J.,Flicker,D.W.and Lilly D.K.,Recovery of three-dimensional wind and temperature fields from single-Doppler radar data.J.Atmos.Sci.,1991,48,876-890.
[68]Kapitza,H.,Numerical experiments with the adjoint of a non-hydrostatic mesoscale model.Mon.Wea.Rev.,1991,119,2993-3011.
[69]Lin,Y.,P.S.Ray,and K.W.Johnson,Initialization of a modeled convective storm using Doppler radar-derived fields.Mon.Wea.Rev.,1993,121,2757-2775.
[70]邱崇践,余金香,Qin xu.多普勒雷达资料对中尺度系统短期预报的改进.气象学报,2000,58,244-249.
[71]李永平,朱国富,薛纪善.应用雷达回波强度资料反演大气云微物理量.气象学报,2004,62(6),814-820.
[72]万齐林,薛纪善,庄世宇.多普勒雷达风场信息变分同化的试验研究.气象学报,2005,63,129-145.
[73]Rinehart,R.E.,Internal storm motions from a single non-Doppler weather radar.Technical Note,NCAR TN-146+STR.National Center for Atmospheric Research,Boulder CO,USA.1979.
[74]Tuttle,J.D.and Foote G.B.,Determination of the boundary layer airflow from a single Doppler radar.J.Atmos.Oceanic Technol.,1990,7,218-232.
[75]张林,倪允琪.雷达径向风资料的四维变分同化试验.大气科学,2006,30(3),433-440.
[76]许小永,刘黎平,郑国光.集合卡尔曼滤波同化多普勒雷达资料的数值试验.大气科学,2006,30(4),712-728.
[77]许小永,郑国光,刘黎平.多普勒雷达资料4DVAR同化反演的模拟研究.气象学报,2004,62,410-422.
[78]顾建峰,颜宏,薛纪善等.多普勒天气雷达资料三维变分直接同化方法研究.博士论文,中国气象科学研究院南京信息工程大学联合培养,2006,228pp.
[79]刘红亚,徐海明,胡志晋,薛纪善,沈桐立.雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅰ:云微物理量和垂直速度的反演.气象学报,2007a,65(6),896-905.
[80]刘红亚,徐海明,薛纪善,胡志晋,沈桐立.雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅱ:数值模拟试验.气象学报,2007b,65(6),906-918.
[81]盛春岩,浦一芬,高守亭.多普勒天气雷达资料对中尺度模式短时预报的影响.大气科学,2006,30(1),93-107.
[82]杨毅,邱崇践,龚建东,邓莲堂,希爽.同化多普勒雷达风资料的两种方法比较.高原气象,2007,26(3),547-555.
[83]闵锦忠,彭霞云,赖安伟,杜宁珠.反演同化和直接同化多普勒雷达径向风的对比试验.南京气象学院学报,2007,30(6),745-754.
[84]李华宏,薛纪善,王曼,杨明.多普勒雷达风廓线的反演及变分同化试验.应用气象学报,2007,18(1),50-57.
[85]李柏,周玉淑,张沛源.新一代天气雷达资料在2003年江淮流域暴雨模拟中的初步应用:模拟降水和风场的对比.大气科学,2007,31(5),826-838.
[86]杨艳蓉,王振会,杨洪平,张沛源,李柏.多普勒雷达反射率与径向风资料在数值模式中的应用试验.气象,2008,34(6),26-34.
[87]杨毅,邱崇践,龚建东,希爽.利用3维变分方法同化多普勒天气雷达资料的试验研究.气象科学,2008a,28(2),124-132.
[88]杨毅,邱崇践,龚建东,黄静.三维变分和物理初始化方法相结合同化多普勒雷达资料的试验研究.气象学报,2008b,66(4),479-488.
[89]Sun,J.,Convective-scale assimilation of radar data:Progress and challenges.Quart.J.Roy.Meteor.Soc.,2005,131,3439-3463.
[90]胡志晋,严采繁.层状云微物理过程的数值模拟(二):中纬度气旋云系的微物理过程.应用气象学报,1987,2(2),133-142.
[91]Liu H.,Xu H.,Hu Zh.,Xue J.,and Shen T.,Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model.Part Ⅰ:Retrieval of Microphysical Elements and Vertical Velocity.Acta Meteorologica Sinica,2008a,22(2),162-172.
[92]Liu H.,Xu H.,Xue J.,Hu Zh.,and Shen T.,Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model.Part Ⅱ:Numerical Simulation Experiments.Acta Meteorologica Sinica,2008b,22(2),173-186.
[93]Probert Jones,J.R.,The Radar Equation in Meteorology.Quarterly Journalof the Royal Meteorological Society,1962,88,485-495.
[94]俞小鼎,姚秀萍,熊廷南,周小刚,吴洪,邓北胜,宋燕.多普勒雷达原理与业务应用.气象出版社,北京,2006,314pp.
[95]张培昌,杜秉玉,戴铁丕.雷达气象学.气象出版社,北京,2001,511pp.
[96]Lorenc,A.,Analysis methods for numerical weather prediction.Quart.J.Roy.Meteor.Soc.,1986,112,1177-1194.
[97]Courtier,P.,J.N.Thépaut,and A.Hollingsworth,A strategy for operational implementation of 4D-VAR,using an incremental approach.Quart.J.Roy.Meteor.Soc.,1994,120,1367-1387.
[98]Lorenc,A.C.,Ballard,S.P.,Bell,R.S.et al.,The met.office global 3-dimensional variational data assimilation scheme,Q.J.Roy.Meteorol.Soc.,2000,26,2991-3012.
[99]薛纪善,陈德辉等.数值预报系统GRAPES的科学设计与应用.科学出版社,北京,2008,383pp.
[100]陈德辉,沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报,2006,17(6),773-777.
[101]张华,薛纪善,庄世宇,朱国富,朱宗申.GRAPES三维变分同化系统的理想试验.气象学报,2004,62(1),31-41.
[102]庄世宇,薛纪善,朱国富,赵军,朱宗申.GRAPES全球三维变分同化系统-基本设计方案与理想试验.大气科学,2005,29(6),872-884.
[103]Biggerstaff,M.I.,and R.A.Houze Jr.,Kinematic and precipitation structure of the 10-11 June 1985 squall line.Mon.Wea.Rev.,1991,119,3034-3065.
I104]Yuter,S.E.,and R.A.Houze,Jr.,Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus.Part Ⅱ:Frequency distribution of vertical velocity,reflectivity,and differential reflectivity.Mon.Wea.Rev.,1995,123,1941-1963.
[105]Kishore Kumar,K.,Jain,A.R.,and Narayana Rao,D.,VHF/UHF radar observations of tropical mesoscale convective systems over southern India,Ann.Geophys.,2005,23,1673-1683.
[106]Milan,M.,Ament,F.,Venema,V.,Simmer,C.,Physical initialization to incorporate radar precipitation data into a numerical weather prediction model(Lokal Model).32nd Conference on Radar Meteorology/11th Conference on Mesoscale Processes.Albuquerque,NM,AMS,22-29 October,2005,JP1-J.15.
[107]Yuter,S.E.,and R.A.Houze,Jr.,Microphysical modes of precipitation growth determined by S-band vertically pointing radar in orographic precipitation during MAP.Quart.J.Roy.Meteor.Soc.,special MAP issue,2003,129,455-476.
[108]洪延超,周非非.”催化-供给”云降水形成机理的数值模拟研究.大气科学,21305,29(6),885-896.
[109]McGinley,J.A.and J.R.Smart,On providing a cloud-balanced initial condition for diabatic initialization.Preprints,18th Conf.on Weather Analysis and Forecasting,Amer.Meteor.Soc.2001.
[110]Byrom,M.and Roulstone,I.,Calculating vertical motion using Richardson's equation.ECMWF/GEWEX workshop on humidity analysis,UK.8-11 July,2002,49-58.
[111]赵思雄,陶祖钰,孙建华,贝耐芳等.长江流域梅雨锋暴雨机理的分析研究.气象出版社,北京,2004,281pp.
[112]陈德辉,胡志晋,徐大海,赵春生,周秀骥等.CAMS大气数值预报模式系统研究.气象出版社,北京,2004,190pp.
[113]Molinari,J.,and M.Dudek,Implicit versus explicit convective heating in numerical weather prediction models.Mon.Wea.Rev.,1986,114,1822-1831.
[114]赵思雄,张立生,孙建华,2007年淮河流域致洪暴雨及其中尺度系统特征的分析.气候与环境研究.2007,12(6),713-727.
[115]丁一汇.高等天气学.气象出版社,北京,2005,585pp.
[116]陈联寿,罗哲贤.影响热带气旋结构和运动的两类因子的数值研究.气象学报,1996,54(4),409-415.
[117]郑庆林,吴军,蒋平.我国东南海岸线分布对9216号台风暴雨增幅影响的数值研究. 热带气象学报,1996a,12(4),304-313.
[118]郑庆林,吴军.地形对9216号台风暴雨增幅影响的数值研究.南京气象学院学报,1996b,19(1),8-17.
[119]周玲丽,高领花,王东法,张陆,翟国庆,陆小勇.台风强暴雨的中尺度分析.浙江大学学报(理学版),2008,35(3),330-335.
[2]张杰,中小尺度天气学.气象出版社,北京,2006,281pp.
[3]Lorenz,E.N.,The predictability of a flow which possesses many scales of motion.Tellus,1969,21,289-307.
[4]Browning,K.A.,The mesoscale data base and its use in mesoscale forecasting.Q.J.R.Met.Soc.,1989,115,717-62.
[5]陆汉城,杨国祥,中尺度天气原理和预报.气象出版社,北京,2004,301pp.
[6]Francois,B.and Ludovic,A.,The potential of data assimilation for nowcasting promises and limits of 3-D variational approach.World weather research program symposium on nowcasting and very short range forecasting,Toulouse,France,5-9th September 2005,P3.05.
[7]Schlatter,T.,Variational assimilation of meteorological observations in the lower atmosphere:atutorial on how it works.J.Atmos.Solar Terrestrial Phys.,2000,62,1057-1070.
[8]Talagrand,O.,Assimilation of Observations,an Introduction.J.Meteor.Soc.Japan.Special lssue 75(1B),1997,191-209.
[9]Richardson,Lewis F.,Weather Prediction by Numerical Process.Cambridge.Cambridge University Press.1922,236pp.Reprinted by Dover Publications,New York,with a new Introduction by Sydney Chapman.1965,236 pp.
[10]Charney,J.G.,Fjφtoft,R.and Von Neumann,J.,Numerical integration of the barotropic vorticity equation.Tellus,1950,2,237-254.
[11]Panofsky,H.A.,Objective weather map analysis.J.Meteor.,1949,6,386-392.
[12]Bergthorsson,P.and B.Doos,Numerical weather map analysis.Tellus,1955,7,329-340.
[13]Cressman,G.P.,An operational objective analysis system.Mon.Wea.Rev.,1959,87,367-374.
[14]Hoke,J.E.and R.A.Anthes,The initialization of numerical models by a dynamic-initialization technique.Mon.Wea.Rev.,1976,104(12),1551-1556.
[15]Kistler,R.E.,A study of data assimilation techniques in an autobarotropic primitive.equation channel model.MS Thesis,Dept of Meteorology,PennState University.1974,84pp.
[16]Eliassen,A.,Provisional report on calculation of spatial covariance and autocorrelation of the pressure field.Report 5,Inst.Weather and Climate Research,Acad.Sci.,Oslo.1954.
[17]Gandin,L.S.,Objective analysis of meteorological fields.Leningrad Hydromet.Press.1963,242pp.
[18]Lewis,J.M.and J.C.Derber,The Use of Adjoint Equations to Solve a Variational Adjustment Problem with Advective Constraints.Tellus,1985,37A,309-327.
[19]Le Dimet,F.X.and O.Talagrand,Variational Algorithms for Analysis and Assimilation of Meteorological Observations.Theoretical Aspects.Tellus,1986,38A,97-110.
[20]Talagrand,O.and P.Courtier,Variational assimilation of meteorological observations with the adjoint vorticity equation.Part 1:Theory.Q.J.R.Meteorol.Soc,' 1987,113,1311-1328.
[21]Courtier,P.and O.Talagrand,Variational assimilation of meteorological observations with the adjoint vorticity equation.Part 2:Numerical results.Q.J.R.Meteorol.Soc,1987,113,1329-1368.
[22]Kalman,R.E.,A new approach to linear filtering and prediction problems.Trans.ASME J.Basic Enging.,1960,82,35-45.
[23]Ghil,M.,Advances in sequential estimation for atmospheric and oceanic flows.J.Meteor.Soc.Japan,1997,75,289-304.
[24]Ide,K.,P.Courtier,M.Ghil,and A.Lorenc,Unified notation for data assimilation:Operational,sequential and variational.J.Meteor.Soc.Japan,1997,75,181-189.
[25]Houtekamer,P.L.,L.Lefaivre,J.Derome,H.Ritchie and H.L.Mitchell,A system simulation approach to ensemble prediction.Mon.Wea.Rev.,1996,124,1225-1242.
[26]Hamill,T.M.and C.Snyder,A hybrid ensemble Kalman filter/3D-variational analysis scheme.Mon.Wea.Rev.,2000,128,2905-2919.
[27]Kalnay,E.,Atmospheric modeling,data assimilation and predictability.Cambridge University Press,United Kingdom,2003,364pp.
[28]Krishnamurti,T.N.et al.,Physical initialization for numerical weather prediction over the tropics.Tellus,1991,43AB,53-81.
[29]Gregoric,G.,Diagnostics of deep convection using radar data and mesoscale numerical model.30th conference on Radar Meteorology,AMS,2001,177-178.
[30]Haase,G.,Crewell,S.,Simmer,C,Wergen,W.,Assimilation of radar data in mesoscale models:Physical initialization and latent heat nudging.Phys Chem Earth(B),2000,25,1237-1242.
[31]Albers,S.C,The LAPS wind analysis.Wea.and.Forecasting,1995,10,342-352.
[32]Xu,M.,N.A.Crook,Y.liu,and R.Rasmussen,Real-time analysis and short term forecasting of snowbands using a mesoscale model.11th Conf.on Aviation,Range and Aerospace Meteo.,Hyannis,MA,Amer.Meteo.Soc.2004.
[33]Jones,C.D.and B.Macpherson,A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model.Meteor.Appl,1997,4,269-277.
[34]Lin,Y.,M.E.Baldwin,K.E.Mitchell,E.Rogers,and G.J.DiMego,Spring 2001 changes to the NCEP Eta Analysis and Forecast System:Assimilation of observed precipitation.18th Conference on Weather Analysis and Forecasting and the 14th Conference on Numerical Weather Prediction,AMS,2001.J92-J95.
[35]Guo,Y.R.,Y.H.Kuo,J.Dudhia,D.Parsons,and C.Rocken,Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case.Mon.Wea.Rev.,2000,128,619-643.
[36]Zupanski,M.,Zupanski,D.,Parrish,D.F.,Rogers,E.and DiMego,G.J.,Four-dimensional variational data assimilation for the blizzard of 2000.Mon.Weather Rev.,2002,130,1967-1988.
[37]Marecal,V.and Mahfouf,J.F.,Experiments on 4D-Var assimilation of rainfall data using an incremental formulation.Q.J.R.Meteorol.Soc,2003,129,3137-3160.
[38]Koizumi,K.,Ishikawa,Y.and Tsuyuki,T.,Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts.SOLA,2005,1,45-48.
[39]Sun,J.and N.A.Crook,Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part Ⅰ.Model development and simulated data experiments.J.Atmos.Sci.,1997,54,1642-1661.
[40]Sun,J.and N.A.Crook,Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part Ⅱ.Retrieval experiments of an observed Florida convective storm.J.Atmos.Sci.,1998,55,835-852.
[41]Sun,J.and N.A.Crook,Real-time low-level wind and temperature analysis using single WSR-88D data.Weather Forecasting,2001,16,117-132.
[42]Crook,N.A.and Sun J.,Assimilating radar,surface and profiler data for the Sydney 2000 forecast demonstration project.J.Atmos.Oceanic Technol,2002,19,888-898.
[43]Sun,J.,Initialization and numerical forecasting of a supercell storm observed during STEPS.Mon.Weather Rev.,2005,133,793-164.
[44]Wu,B.,J.Verlinde,and J.Sun,Dynamical and microphysical retrievals from Doppler radar observations of a deep convective cloud.J.Atmos.Sci.,2000,57,262-283.
[45]Xiao,Q.,Y.H.Kuo,J.Sun,W.C.Lee,D.M.Barker,and E.Lim,An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa(2002)at landfall.J.Appl.Meteor.Climatol,2007,46,14-22.
[46]Xiao,Q.,and J.Sun,Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002.Mon.Wea.Rev.,2007,135,3381-3404.
[47]Snyder,C.and Zhang,F.,Assimilation of simulated Doppler radar observations with an ensemble Kalman filter.Mon.Weather Rev.,2003,131,1663-1677.
[48]Zhang,F.,Snyder,C.and Sun,J.,Impacts of initial estimate and observations on convective-scale data assimilations.Mon.Weather Rev.,2004,132,1238-1253.
[49]Caya,A.,Sun J.and Snyder,C,A comparison between the 4D-Var and the ensemble Kalman filter techniques for radar data assimilation.Mon.Weather Rev.,2005,133,3081-3094.
[50]Lhermitte,R.M.,and D.Atlas,Precipitation motion by pulse Doppler.Preprints Ninth Weather Radar Conf.,Amer.Meteor.Soc,1961,218-223.
[51]Browning,K.A.,and R.Wexler,The determination of kinematic properties of a wind field using Doppler radar.J.Applied.Meteor.,1968,7,105-113.
[52]Srivastava,R.C,T.J.Matejka and T.J.Lorello.,Doppler radar study of the trailing anvil region associated with a squall line.Journal of the Atmospheric Sciences,1986,43,356-377.
[53]Matejka,T.,and R.C.Srivastava.,An improved version of the extended velocity-azimuth display analysis of Single-doppler radar data.Journal of Atmospheric and Oceanic Technology,1991,8,453-466.
[54]Waldteufel,P.and H.Corbin,On the analysis of single-Doppler radar data,J.Appl.Meteor.,1979,18,532-542.
[55]McGinley,J.A.,The local analysis and prediction system.Pre-prints,12th Conf.On Weather Analysis and Forecasting,Monterey,CA,Amer.Meteor.Soc,1989,15-20.
[56]Brewster,K.,Implementation of a Bratseth analysis scheme including Doppler radar.15th conference on Weather Analysis and Forecasting,1996,92-95.
[57]Benjamin,S.G.,K.A.Brewster,R.Brummer,B.F.Jewett,T.W.Schlatter,T.L.Smith,and P.A.Stamus,An isentropic meso-scale analysis system and its sensitivity to aircraft and surface observations.Mon.Wea.Rev.,1991,117,1586-1603.
[58]Liou,Y.C.,Retrieval of three-dimensional wind and temperature fields from one component wind data by using the four-dimensional data assimilation technique.Master's thesis,University of Oklahoma,1990,112pp.
[59]Charney,J.,M.Halem,and R.Jastrow,Use of incomplete historical data to infer the present state of the atmosphere.J.Atmos.Sci.,1969,26,1160-1163.
[60]Gal-Chen,T.,A method for the initialization of the anelastic equations:Implications for matching models with observations.Mon.Wea.Rev.,1978,106,587-602.
[61]Parrish,D.F.and J.Purser,Anisotropic covariances in 3D-Var:Application to hurricane Doppler radar observations.HIRLAM 4 workshop on Variational analysis in limited area models,Meteo-France,Toulouse.1998.
[62]Lindskog,M.,H.Jarvinen,and D.B.Michelson,Assimilation of radar radial wind in the HIRLAM 3D-Vat.Phys.Chem.Earth(B),2000,25,1243-1250.
[63]Lindskog,M.,K.Salonen,H.Jarvinen,and D.B.Michelson,Doppler radar wind data Assimilation with HIRLAM 3DVAR.Mon.Wea.Rev.,2004,132,1081-1092.
[64]Lindskog M.,H.Jarvinen,and D.B.Michelson,Development of Doppler radar wind data assimilation for the HIRLAM 3D-Vat.HIRLAM Technical Report 52,HIRLAM-5 Project.2002.
[65]Xiao,Q.,Y.H.Kuo,J.Sun,W.C.Lee,E.Lira,Y.R.Guo,and D.M.Barker,Assimilation of Doppler radar observations with a regional 3D-Var system:Impact of Doppler velocities on forecasts of a heavy rainfall case.J.Appl.Meteor.,2005,44,768-788.
[66]Wolfsberg,D.,Retrieval of three-dimensional wind and temperature fields from single-Doppler radar data.CIMMS Report No.84,Cooperative Institute for Mesoscale Meteorological Studies,401 East Boyd,100 East Boyd St.,OK,USA.1987.
[67]Sun,J.,Flicker,D.W.and Lilly D.K.,Recovery of three-dimensional wind and temperature fields from single-Doppler radar data.J.Atmos.Sci.,1991,48,876-890.
[68]Kapitza,H.,Numerical experiments with the adjoint of a non-hydrostatic mesoscale model.Mon.Wea.Rev.,1991,119,2993-3011.
[69]Lin,Y.,P.S.Ray,and K.W.Johnson,Initialization of a modeled convective storm using Doppler radar-derived fields.Mon.Wea.Rev.,1993,121,2757-2775.
[70]邱崇践,余金香,Qin xu.多普勒雷达资料对中尺度系统短期预报的改进.气象学报,2000,58,244-249.
[71]李永平,朱国富,薛纪善.应用雷达回波强度资料反演大气云微物理量.气象学报,2004,62(6),814-820.
[72]万齐林,薛纪善,庄世宇.多普勒雷达风场信息变分同化的试验研究.气象学报,2005,63,129-145.
[73]Rinehart,R.E.,Internal storm motions from a single non-Doppler weather radar.Technical Note,NCAR TN-146+STR.National Center for Atmospheric Research,Boulder CO,USA.1979.
[74]Tuttle,J.D.and Foote G.B.,Determination of the boundary layer airflow from a single Doppler radar.J.Atmos.Oceanic Technol.,1990,7,218-232.
[75]张林,倪允琪.雷达径向风资料的四维变分同化试验.大气科学,2006,30(3),433-440.
[76]许小永,刘黎平,郑国光.集合卡尔曼滤波同化多普勒雷达资料的数值试验.大气科学,2006,30(4),712-728.
[77]许小永,郑国光,刘黎平.多普勒雷达资料4DVAR同化反演的模拟研究.气象学报,2004,62,410-422.
[78]顾建峰,颜宏,薛纪善等.多普勒天气雷达资料三维变分直接同化方法研究.博士论文,中国气象科学研究院南京信息工程大学联合培养,2006,228pp.
[79]刘红亚,徐海明,胡志晋,薛纪善,沈桐立.雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅰ:云微物理量和垂直速度的反演.气象学报,2007a,65(6),896-905.
[80]刘红亚,徐海明,薛纪善,胡志晋,沈桐立.雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅱ:数值模拟试验.气象学报,2007b,65(6),906-918.
[81]盛春岩,浦一芬,高守亭.多普勒天气雷达资料对中尺度模式短时预报的影响.大气科学,2006,30(1),93-107.
[82]杨毅,邱崇践,龚建东,邓莲堂,希爽.同化多普勒雷达风资料的两种方法比较.高原气象,2007,26(3),547-555.
[83]闵锦忠,彭霞云,赖安伟,杜宁珠.反演同化和直接同化多普勒雷达径向风的对比试验.南京气象学院学报,2007,30(6),745-754.
[84]李华宏,薛纪善,王曼,杨明.多普勒雷达风廓线的反演及变分同化试验.应用气象学报,2007,18(1),50-57.
[85]李柏,周玉淑,张沛源.新一代天气雷达资料在2003年江淮流域暴雨模拟中的初步应用:模拟降水和风场的对比.大气科学,2007,31(5),826-838.
[86]杨艳蓉,王振会,杨洪平,张沛源,李柏.多普勒雷达反射率与径向风资料在数值模式中的应用试验.气象,2008,34(6),26-34.
[87]杨毅,邱崇践,龚建东,希爽.利用3维变分方法同化多普勒天气雷达资料的试验研究.气象科学,2008a,28(2),124-132.
[88]杨毅,邱崇践,龚建东,黄静.三维变分和物理初始化方法相结合同化多普勒雷达资料的试验研究.气象学报,2008b,66(4),479-488.
[89]Sun,J.,Convective-scale assimilation of radar data:Progress and challenges.Quart.J.Roy.Meteor.Soc.,2005,131,3439-3463.
[90]胡志晋,严采繁.层状云微物理过程的数值模拟(二):中纬度气旋云系的微物理过程.应用气象学报,1987,2(2),133-142.
[91]Liu H.,Xu H.,Hu Zh.,Xue J.,and Shen T.,Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model.Part Ⅰ:Retrieval of Microphysical Elements and Vertical Velocity.Acta Meteorologica Sinica,2008a,22(2),162-172.
[92]Liu H.,Xu H.,Xue J.,Hu Zh.,and Shen T.,Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model.Part Ⅱ:Numerical Simulation Experiments.Acta Meteorologica Sinica,2008b,22(2),173-186.
[93]Probert Jones,J.R.,The Radar Equation in Meteorology.Quarterly Journalof the Royal Meteorological Society,1962,88,485-495.
[94]俞小鼎,姚秀萍,熊廷南,周小刚,吴洪,邓北胜,宋燕.多普勒雷达原理与业务应用.气象出版社,北京,2006,314pp.
[95]张培昌,杜秉玉,戴铁丕.雷达气象学.气象出版社,北京,2001,511pp.
[96]Lorenc,A.,Analysis methods for numerical weather prediction.Quart.J.Roy.Meteor.Soc.,1986,112,1177-1194.
[97]Courtier,P.,J.N.Thépaut,and A.Hollingsworth,A strategy for operational implementation of 4D-VAR,using an incremental approach.Quart.J.Roy.Meteor.Soc.,1994,120,1367-1387.
[98]Lorenc,A.C.,Ballard,S.P.,Bell,R.S.et al.,The met.office global 3-dimensional variational data assimilation scheme,Q.J.Roy.Meteorol.Soc.,2000,26,2991-3012.
[99]薛纪善,陈德辉等.数值预报系统GRAPES的科学设计与应用.科学出版社,北京,2008,383pp.
[100]陈德辉,沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报,2006,17(6),773-777.
[101]张华,薛纪善,庄世宇,朱国富,朱宗申.GRAPES三维变分同化系统的理想试验.气象学报,2004,62(1),31-41.
[102]庄世宇,薛纪善,朱国富,赵军,朱宗申.GRAPES全球三维变分同化系统-基本设计方案与理想试验.大气科学,2005,29(6),872-884.
[103]Biggerstaff,M.I.,and R.A.Houze Jr.,Kinematic and precipitation structure of the 10-11 June 1985 squall line.Mon.Wea.Rev.,1991,119,3034-3065.
I104]Yuter,S.E.,and R.A.Houze,Jr.,Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus.Part Ⅱ:Frequency distribution of vertical velocity,reflectivity,and differential reflectivity.Mon.Wea.Rev.,1995,123,1941-1963.
[105]Kishore Kumar,K.,Jain,A.R.,and Narayana Rao,D.,VHF/UHF radar observations of tropical mesoscale convective systems over southern India,Ann.Geophys.,2005,23,1673-1683.
[106]Milan,M.,Ament,F.,Venema,V.,Simmer,C.,Physical initialization to incorporate radar precipitation data into a numerical weather prediction model(Lokal Model).32nd Conference on Radar Meteorology/11th Conference on Mesoscale Processes.Albuquerque,NM,AMS,22-29 October,2005,JP1-J.15.
[107]Yuter,S.E.,and R.A.Houze,Jr.,Microphysical modes of precipitation growth determined by S-band vertically pointing radar in orographic precipitation during MAP.Quart.J.Roy.Meteor.Soc.,special MAP issue,2003,129,455-476.
[108]洪延超,周非非.”催化-供给”云降水形成机理的数值模拟研究.大气科学,21305,29(6),885-896.
[109]McGinley,J.A.and J.R.Smart,On providing a cloud-balanced initial condition for diabatic initialization.Preprints,18th Conf.on Weather Analysis and Forecasting,Amer.Meteor.Soc.2001.
[110]Byrom,M.and Roulstone,I.,Calculating vertical motion using Richardson's equation.ECMWF/GEWEX workshop on humidity analysis,UK.8-11 July,2002,49-58.
[111]赵思雄,陶祖钰,孙建华,贝耐芳等.长江流域梅雨锋暴雨机理的分析研究.气象出版社,北京,2004,281pp.
[112]陈德辉,胡志晋,徐大海,赵春生,周秀骥等.CAMS大气数值预报模式系统研究.气象出版社,北京,2004,190pp.
[113]Molinari,J.,and M.Dudek,Implicit versus explicit convective heating in numerical weather prediction models.Mon.Wea.Rev.,1986,114,1822-1831.
[114]赵思雄,张立生,孙建华,2007年淮河流域致洪暴雨及其中尺度系统特征的分析.气候与环境研究.2007,12(6),713-727.
[115]丁一汇.高等天气学.气象出版社,北京,2005,585pp.
[116]陈联寿,罗哲贤.影响热带气旋结构和运动的两类因子的数值研究.气象学报,1996,54(4),409-415.
[117]郑庆林,吴军,蒋平.我国东南海岸线分布对9216号台风暴雨增幅影响的数值研究. 热带气象学报,1996a,12(4),304-313.
[118]郑庆林,吴军.地形对9216号台风暴雨增幅影响的数值研究.南京气象学院学报,1996b,19(1),8-17.
[119]周玲丽,高领花,王东法,张陆,翟国庆,陆小勇.台风强暴雨的中尺度分析.浙江大学学报(理学版),2008,35(3),330-335.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |