多普勒雷达四维变分分析系统概述

顾建峰; 薛纪善; 颜宏

多普勒雷达四维变分分析系统概述

1.
中国气象科学研究院, 北京, 100081;上海中心气象台, 上海, 200030;中国气象科学研究院, 北京, 100081
2.
中国气象科学研究院, 北京, 100081;上海中心气象台, 上海, 200030
3.
World Meteorological Organization, Geneva, Switzerland

基金项目: 国家自然科学基金项目40175028,40233036资助

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出版历程
- 收稿日期: 2003-04-21
- 修回日期: 2003-07-21

A SUMMARIZATION OF THE FOUR-DIMENSIONAL VARIATIONAL DOPPLER RADAR ANALYSIS SYSTEM

1.
Chinese Academy of Meteorological Science, Beijing 100081, China;Shanghai Weather Forecast Center, Shanghai 20030, China
2.
Chinese Academy of Meteorological Science, Beijing 100081, China
3.
World Meteorological Organization, Geneva, Switzerland

摘要

摘要: NCAR(National Center for Atmospheric Research)在1990年代发展起来的多普勒雷达四维变分分析系统(The four-dimensional VariationalDoppler Radar Analysis System,简称 VDRAS),采用四维变分(4D-VAR)资料同化技术和云尺度数值模式及其伴随模式,利用单部或多部多普勒雷达观测资料,反演对流尺度风暴的动力结构和微物理结构,包括三维风场、温度场、气压场和微物理量场。本文介绍了VDRAS的基本原理、个例试验和实时运行等概况,旨在随着我国新一代天气雷达建设的逐步完成,为应用我国新一代天气雷达的观测资料,开展4D-VAR方法的研究和应用提供一些参考或借鉴。
- 多普勒雷达 /
- 四维变分 /
- 资料同化
Abstract: In 1990s, a four-dimensional variational Doppler radar analysis system (VDRAS) has been developed and implemented in NCAR. This analysis system applies the four-dimensional variational data assimilation technique to a cloud-scale model. VDRAS can be used to retrieve three-dimensional wind, temperature, pressure, and microphysical fields in convective storms by assimilating data from one or more Doppler radars. This paper aims at summarizing VDRAS' basic principle, cases study, and real-time running. With the accomplishment of China's new generation weather radar project step by step, this paper hopes to provide some references in 4D-VAR research and application using China CINRAD radar observations.
- Doppler radar /
- four-dimensional variation /
- data assimilation

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参考文献(47)

[1]	LHERMITTE R M, ATLAS D. Precipitation motion by pulse Doppler radar[C].Preprints, Ninth Weather Radar Conf, KansasCity, MO, Amer Meteor Soc, 1961. 218-223.
[2]	BROWNING K A, WEXLER R. A determination of kinematic properties of a wind field using Doppler radar[J].J ApplMeteor, 1968, 7: 105-113.
[3]	WALDTEUFEL P, CORBIN H. On the analysis of single-Doppler radar data[J].J Appl Meteor, 1979, 18: 532-542.
[4]	KOSCIELNY A J, DOVIAK R J, Rabin R. Statistical considerations in the estimation of divergence from single-Dopplerradar and application to prestorm boundary-layer observations[J].J Appl Meteor, 1982, 21: 197-210.
[5]	陶祖钰. 从单Doppler速度场反演风矢量场的VAP方法[J].气象学报, 1992, 50: 81-90.
[6]	郎需兴, 魏鸣, 葛文忠, 等. 一种新的单多普勒雷达风场反演方法[J].气象科学, 2001, 21: 417-424.
[7]	姜海燕, 葛润生. 一种新的单部多普勒雷达反演技术[J].应用气象学报, 1997, 8: 219-223.
[8]	ZAWADZKI I I. Statistical properties of precipitation patterns[J].J Appl Meteor, 1973, 12: 459-472.
[9]	QIU C J, XU Q. A simple adjoint method of wind analysis for single-Doppler data[J].J Atmos Oceanic Technol, 1992, 9:588-598.
[10]	XU Q, QIU C J, YU J X. Adjoint-method retrievals of low-altitude wind fields from single-Doppler reflectivitymeasured during PhoenixⅡ[J].J Atmos Oceanic Technol, 1994, 11: 275-288.
[11]	XU Q, QIU C J, YU J X. Adjoint-method retrievals of low-altitude wind fields from single-Doppler wind data[J] .J Atmos Oceanic Technol, 1994, 11: 579-585.
[12]	LAROCHE S, ZAWADZKI I. A variational analysis method for retrieval of three-dimensional wind field from single-Doppler radar data[J].J Atmos Sci, 1994, 51: 2664-2684.
[13]	SHAPIRO A, ELLIS S, SHAW J. Single-Doppler velocity retrievals with PhoenixⅡdata: Clear air and microburstwind retrievals in the planetary boundary layer[J].J Atmos Sci, 1995, 52: 1265-1287.
[14]	XU Q, QIU C J. Adjoint-method retrievals of low-altitude wind fields from single-Doppler reflectivity and radial-winddata[J].J Atmos Oceanic Technol, 1995, 12: 1111-1119.
[15]	邱崇践, XU Q. 由单Doppler雷达资料反演水平风场的简单共轭函数方法的改进方案[J].应用气象学报, 1996, 7:421-430.
[16]	邱崇践, 余金香, Xu Q. 多普勒雷达资料对中尺度系统短期预报的改进[J].气象学报, 2000, 58: 244-249.
[17]	GAL-CHEN T. A method for the initialization of the anelastic equations: Implications for matching modelswith observation[J].Mon Wea Rev, 1978, 106: 587-606.
[18]	HANE C E, SCOTT B C. Temperature and pressure perturbations within convective clouds derived from detailed airmotions: Preliminary testings[J].Mon Wea Rev, 1978, 106: 654-661.
[19]	HANE C E, WILHELMSON R B, GAL-CHEN T. Retrieval of thermodynamic variables within deep convectiveclouds: Experiments in three dimensions[J].Mon Wea Rev, 1981, 109: 564-576.
[20]	GAL-CHEN T, KROPFLI R A. Buoyancy and pressure perturbations derived from dual-Doppler radar observations ofthe planetary boundary layer: applications for matching models with observations[J].J Atmos Sci, 1984, 41: 3007-3020.
[21]	ROUX F. Retrieval of thermodynamic fields from multiple-Doppler radar data using the equation of motion andthe thermodynamic equation[J].Mon Wea Rev, 1985, 113: 2142-2157.
[22]	HAUSER D, AMAYENC P. Retrieval of cloud water and water vapor contents from Doppler-radar data in a tropicalsquall line[J].J Atmos Sci, 1986, 43: 823-838.
[23]	RUTLEDGE S A, HOBBS P V. The mesoscale and microscale structure and organization of clouds and precipitation inmid-latitude cyclones. Ⅷ: A model for the "seeder-feeder" process in warm-frontal rainbands[J].J Atmos Sci, 1983,40: 1185-1206.
[24]	RUTLEDGE S A, HOBBS P V. The mesoscale and microscale structure and organization of clouds and precipitation inmid-latitude cyclones. Ⅻ: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands[J] .J Atmos Sci, 1984, 41: 2949-2972.
[25]	ZIEGLER C L. Retrieval of thermal and microphysical variables in observed convective storms. Part Ⅰ: Modeldevelopment and preliminary testing[J].J Atmos Sci, 1985, 42: 1487-1509.
[26]	ZIEGLER C L. Retrieval of thermal and microphysical variables in observed convective storms. Part Ⅱ: Sensitivity ofcloud processes to variation of the microphysical parameterization[J].J Atmos Sci, 1988, 45: 1072-1090.
[27]	LEWIS J M, DERBER J C. The use of adjoint equations to solve a variational adjustment problem withadvective constraints[J].Tellus, 1985, 37A: 309-322.
[28]	TALAGRAND O, COURTIER P. Variational assimilation of meteorological observations with the adjoint vorticityequation.Ⅰ: Theory. Quart[J].J Roy Meteor Soc, 1987, 113: 1311-1328.
[29]	WOLFSBERG D G. Retrieval of three-dimensional wind and temperature fields from single-Doppler radar data[D] .Ph D thesis, University of Oklahoma, 1987, 91.
[30]	KAPITZA H. Numerical experiments with the adjoint of a nonhydrostatic mesoscale model[J].Mon Wea Rev, 1991,119: 2993-3011.
[31]	SUN J, FLICKER D, Lilly D. Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data[J].J Atmos Sci, 1991, 48: 876-890.
[32]	SUN J, CROOK N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud modeland its adjoint. PartⅠ: Model development and simulated data experiments[J].J Atmos Sci, 1997, 54: 1642-1661.
[33]	WU B, VERLINDE J, SUN J. Dynamical and microphysical retrievals from Doppler radar observations of a deepconvective cloud[J].J Atmos Sci, 2000, 57: 262-283.
[34]	SUN J, CROOK N A. Real-time low-level wind and temperature analysis using single WSR-88D data[J].WeaForecasting, 2001, 16: 117-132.
[35]	VERLINDE J, COTTON W R. A critical look at kinematic microphysical retrieval algorithms[C].Preprints, Conf onCloud Physics, San Francisco, CA, Amer Meteor Soc, 1990. 453-457.
[36]	SUN J. Fitting a Cartesian prediction model to radial velocity data from single-Doppler radar[J].J Atmos OceanicTechnol, 1994, 11: 200-204.
[37]	SUN J, CROOK N A. Wind and thermodynamic retrieval from single-Doppler measurements of a gust front observedduring PhoenixⅡ[J].Mon Wea Rev, 1994, 122: 1075-1091.
[38]	VERLINDE J, COTTON W R. Fitting microphysical observations of nonsteady convective clouds to a numerical model:An application of the adjoint technique of data assimilation to a kinematic model[J].Mon Wea Rev, 1993, 121: 2776-2793.
[39]	ORVILLE H D, KOPP F J. Numerical simulation of the life history of a hailstorm[J].J Atmos Sci, 1977, 34: 1596-1618.
[40]	LIN Y L, FARLEY R D, Orville H D. Bulk parameterization of the snow field in a cloud model[J].J Climate ApplMeteor, 1983, 22: 1065-1089.
[41]	TRIPOLI G J, COTTON W R. The use of ice-liquid water potential temperature as a thermodynamic variable indeep atmospheric models[J].Mon Wea Rev, 1981, 109: 1094-1102.
[42]	JING Z, WIENER G. Two-dimensional dealiasing of Doppler velocities[J].J Atmos Oceanic Technol, 1993, 10: 798-808.
[43]	BARNES S. A technique for maximizing details in numerical map analysis[J].J Appl Meteor, 1964, 3: 395-409.
[44]	SUN J, CROOK N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud modeland its adjoint. PartⅡ: Retrieval experiments of an observed Florida convective storm[J].J Atmos Sci, 1998, 55: 835-852.
[45]	WARNER T T, BRANDES E E, MUELLER C K, et al. Prediction of a flash flood in complex terrain. PartⅠ: Acomparison of rainfall estimates from radar, and very-short-range rainfall simulations from a dynamic modeland an automated algorithmic system[J].J Appl Meteor, 2000, 39: 797-814.
[46]	YATES D N, WARNER T T, LEAVESLEY G H. Prediction of a flash flood in complex terrain. PartⅡ: A comparison offlood discharge simulations using rainfall input from radar, a dynamic model and an automated algorithmic system[J].J Appl Meteor, 2000, 39: 815-825.
[47]	CROOK N A, SUN J. Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project[J] .J Atmos Oceanic Technol, 2002, 19: 888-898.