雷达观测对应模式变量非线性特征及对四维变分同化的影响
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摘要
四维变分同化(4DVar)中切线性模式和伴随模式的时间积分长度即为同化时间窗的长度。为理解线性模式时间积分长度对4DVar的具体影响,在雷达观测对应变量非线性分析的基础上,进行了一系列不同时间窗(10 min、20 min和30 min)4DVar单点观测试验和一次降雨的实际雷达同化和预报试验。从径向风同化来看:短时间窗(10 min)的风场增量更大、更局地;长时间窗(20 min、30 min)的风场增量则更具系统性特征,但会丢失一些小尺度信息,导致暴雨预报能力降低。从反射率同化来看:短时间窗对6 h内强降水预报有较明显的改善,较长时间窗甚至会降低降水预报效果。研究旨在为合理设置4DVar的同化时间窗提供参考,以有效利用高时空分辨率的雷达观测资料,又尽量减小线性化造成的误差,进而快速有效地同化雷达信息。
The time integral length of tangent-linear and adjoint model in 4 DVar is the length of the assimilation window. To understand the effect of time integral length on 4 DVar, a series of 4 DVar single observation experiments with different time windows(10 min, 20 min, 30 min) and a real rainfall case had been carried out based on the nonlinearity analysis of radar observations. In terms of radial velocity assimilation, the wind increments of the short-time window(10 min) is larger and more localized; while the long-time window brings broader and more systemic features, but some small-scale information will be lost,which leads to the decrease of the ability of heavy rain prediction. As to reflectivity, The 10 min-window experiment has a distinct improvement on the forecast of heavy rainfall in 6 hours while the long window(20 min, 30 min) even degrades the precipitation forecasts. The purpose of this study is to provide a reference for the reasonable setting of the 4 DVar assimilation window so as to make good use of radar observations with high spatial and temporal resolutions, and to minimize the error caused by linearization so as to assimilate the radar information quickly and effectively.
The time integral length of tangent-linear and adjoint model in 4 DVar is the length of the assimilation window. To understand the effect of time integral length on 4 DVar, a series of 4 DVar single observation experiments with different time windows(10 min, 20 min, 30 min) and a real rainfall case had been carried out based on the nonlinearity analysis of radar observations. In terms of radial velocity assimilation, the wind increments of the short-time window(10 min) is larger and more localized; while the long-time window brings broader and more systemic features, but some small-scale information will be lost,which leads to the decrease of the ability of heavy rain prediction. As to reflectivity, The 10 min-window experiment has a distinct improvement on the forecast of heavy rainfall in 6 hours while the long window(20 min, 30 min) even degrades the precipitation forecasts. The purpose of this study is to provide a reference for the reasonable setting of the 4 DVar assimilation window so as to make good use of radar observations with high spatial and temporal resolutions, and to minimize the error caused by linearization so as to assimilate the radar information quickly and effectively.
引文
[1]孙娟珍,陈明轩,范水勇.雷达资料同化方法:回顾与前瞻[J].气象科技进展, 2016, 6(3):17-27.
[2] GAO J D, XUE M, BREWSTER K, et al. A three-dimensional variational data analysis method with recursive filter for Doppler radars[J]. JAtmos Ocean Technol, 2004, 21(3):457-469.
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[4]万齐林,薛纪善,庄世宇,等.多普勒雷达风场信息变分同化的试验研究[J].气象学报, 2005, 63(2):129-145.
[5]杨毅,邱崇践,龚建东,等.利用3维变分方法同化多普勒天气雷达资料的试验研究[J].气象科学, 2008, 28(2):124-132.
[6]刘红亚,薛纪善,顾建峰,等.三维变分同化雷达资料暴雨个例试验[J].气象学报, 2010, 68(6):779-789.
[7]高郁东,万齐林,何金海.三维变分同化雷达视风速的改进方案及其数值试验[J].气象学报, 2011, 69(4):631-643.
[8] SUN J, CROOK N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part I.model development and simulated data experiments[J]. J Atmos Sci, 1997, 54(12):1 642-1 661.
[9] WANG H, SUN J, ZHANG X, et al. Radar Data Assimilation with WRF 4D-Var. Part I:System Development and Preliminary Testing[J].Mon Wea Rev, 2013, 141(7):2 224-2 244.
[10]张林,倪允琪.雷达径向风资料的四维变分同化试验[J].大气科学, 2006, 30(3):433-440.
[11]李磊,江崟,张文海,等.基于四维数据同化技术的千米格距网格化气象数据集:构建及初步应用[J].热带气象学报,2017,33(6):874-883.
[12]高士博,闵锦忠,黄丹莲. EnKF局地化算法对雷达资料同化的影响研究[J].大气科学学报, 2016,39(5):633-642.
[13] TONG M, XUE M. Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemblesquare root kalman filter. part II:Parameter estimation experiments[J]. Mon Wea Rev, 2008, 136(5):1 649-1 668.
[14] ZHANG F, SNYDER C, SUN J. Impacts of initial estimate and observation availability on convective-scale data assimilation with anensemble kalman filter[J]. Mon Wea Rev, 2004, 132(5):1 238-1 253.
[15]兰伟仁,朱江,薛明,等.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验Ⅰ:不考虑模式误差的情形[J].大气科学,2010, 34(4):640-652.
[16]朱磊,万齐林,刘靓珂,等.雷达径向风资料EnKF同化应用对Vicente(2012)登陆台风强度变化过程预报的影响试验研究[J].热带气象学报,2017,33(3):345-356.
[17] SUN J, WANG H. Radar data assimilation with WRF 4D-Var Part II:Comparison with 3D-Var for a squall line over the U.S. GreatPlains[J]. Mon Wea Rev, 2013, 141(7):2 245-2 264.
[18] GAO J, STENSRUD D J. Some observing system simulation experiments with a Hybrid 3DEnVAR system for storm-scale radar dataassimilation[J]. Mon Wea Rev, 2014, 142(9):3 326-3 346.
[19] AKSOY A, DOWELL D C, SNYDER C. A multicase comparative assessment of the ensemble kalman filter for assimilation of radarobservations. Part II:Short-Range ensemble forecasts[J]. Mon Wea Rev, 2010, 138(4):1 273-1 292.
[20] YUSSOUF N, STENSRUD D J. Comparison of single-parameter and multiparameter ensembles for assimilation of radar observationsusing the Ensemble Kalman Filter[J]. Mon Wea Rev, 2010, 140(2):562-586.
[21]陈明轩,高峰,孙娟珍,等.基于VDRAS的快速更新雷达四维变分分析系统[J].应用气象学报, 2016, 27(3):257-272
[22] FABRY F, SUN J. for how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I:On thepropagation of initial condition errors and their implications for data assimilation[J]. Mon Wea Rev, 2010, 138(1):242-255.
[23] FABRY F. For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part II:On theobservation signal from different sensors[J]. Mon Wea Rev, 2010, 138(1):256-264.
[24] GAO J, STENSRUD D J. Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeorclassification[J]. J Atmos Sci, 2011, 69(3):1 054-1 065.
[25] WANG H, SUN J, FAN S, et al. Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of foursummertime convective events[J]. Journal of Applied Meteorology&Climatology, 2013, 52(4):889-902.
[26] PARRISH D F,DERBER J C. The national meteorological center's spectral statistical-interpolation analysis system[J]. Mon Wea Rev,1992, 120(8):1 747-1 763.
[27] CHEN Y, XIA X, MIN J, et al. Balance characteristics of multivariate background error covariance for rainy and dry seasons and theirimpact on precipitation forecasts of two rainfall events[J]. Meteorology&Atmospheric Physics, 2016, 128(5):1-22.
[28]陈耀登,夏雪,闵锦忠,等.背景误差协方差变量平衡特征及其对台风同化和预报的影响[J].热带气象学报,2017,33(3):289-298.
[29] SUN J, WANG H, TONG W, et al. Comparison of the impacts of momentum control variables on high-resolution variational dataassimilation and precipitation forecasting[J]. Mon Wea Rev, 2016, 144(1):149-169.
[30] LIM E, SUN J. A Velocity dealiasing technique using rapidly updated analysis from a four-dimensional variational doppler radar dataassimilation system[J]. J Atmos Ocean Technol, 2010, 27(7):1 140-1 152.
[31] ROBERTS N M, LEAN H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J].Mon Wea Rev, 2008, 136(1):78-97.
[2] GAO J D, XUE M, BREWSTER K, et al. A three-dimensional variational data analysis method with recursive filter for Doppler radars[J]. JAtmos Ocean Technol, 2004, 21(3):457-469.
[3] XIAO Q, KUO Y H, SUN J, et al. Assimilation of Doppler radar observations with a regional 3D-Var system:Impact of Doppler velocitieson forecasts of a heavy rainfall case[J]. J Appl Meteor, 2005, 44(6):768-788.
[4]万齐林,薛纪善,庄世宇,等.多普勒雷达风场信息变分同化的试验研究[J].气象学报, 2005, 63(2):129-145.
[5]杨毅,邱崇践,龚建东,等.利用3维变分方法同化多普勒天气雷达资料的试验研究[J].气象科学, 2008, 28(2):124-132.
[6]刘红亚,薛纪善,顾建峰,等.三维变分同化雷达资料暴雨个例试验[J].气象学报, 2010, 68(6):779-789.
[7]高郁东,万齐林,何金海.三维变分同化雷达视风速的改进方案及其数值试验[J].气象学报, 2011, 69(4):631-643.
[8] SUN J, CROOK N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint:Part I.model development and simulated data experiments[J]. J Atmos Sci, 1997, 54(12):1 642-1 661.
[9] WANG H, SUN J, ZHANG X, et al. Radar Data Assimilation with WRF 4D-Var. Part I:System Development and Preliminary Testing[J].Mon Wea Rev, 2013, 141(7):2 224-2 244.
[10]张林,倪允琪.雷达径向风资料的四维变分同化试验[J].大气科学, 2006, 30(3):433-440.
[11]李磊,江崟,张文海,等.基于四维数据同化技术的千米格距网格化气象数据集:构建及初步应用[J].热带气象学报,2017,33(6):874-883.
[12]高士博,闵锦忠,黄丹莲. EnKF局地化算法对雷达资料同化的影响研究[J].大气科学学报, 2016,39(5):633-642.
[13] TONG M, XUE M. Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemblesquare root kalman filter. part II:Parameter estimation experiments[J]. Mon Wea Rev, 2008, 136(5):1 649-1 668.
[14] ZHANG F, SNYDER C, SUN J. Impacts of initial estimate and observation availability on convective-scale data assimilation with anensemble kalman filter[J]. Mon Wea Rev, 2004, 132(5):1 238-1 253.
[15]兰伟仁,朱江,薛明,等.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验Ⅰ:不考虑模式误差的情形[J].大气科学,2010, 34(4):640-652.
[16]朱磊,万齐林,刘靓珂,等.雷达径向风资料EnKF同化应用对Vicente(2012)登陆台风强度变化过程预报的影响试验研究[J].热带气象学报,2017,33(3):345-356.
[17] SUN J, WANG H. Radar data assimilation with WRF 4D-Var Part II:Comparison with 3D-Var for a squall line over the U.S. GreatPlains[J]. Mon Wea Rev, 2013, 141(7):2 245-2 264.
[18] GAO J, STENSRUD D J. Some observing system simulation experiments with a Hybrid 3DEnVAR system for storm-scale radar dataassimilation[J]. Mon Wea Rev, 2014, 142(9):3 326-3 346.
[19] AKSOY A, DOWELL D C, SNYDER C. A multicase comparative assessment of the ensemble kalman filter for assimilation of radarobservations. Part II:Short-Range ensemble forecasts[J]. Mon Wea Rev, 2010, 138(4):1 273-1 292.
[20] YUSSOUF N, STENSRUD D J. Comparison of single-parameter and multiparameter ensembles for assimilation of radar observationsusing the Ensemble Kalman Filter[J]. Mon Wea Rev, 2010, 140(2):562-586.
[21]陈明轩,高峰,孙娟珍,等.基于VDRAS的快速更新雷达四维变分分析系统[J].应用气象学报, 2016, 27(3):257-272
[22] FABRY F, SUN J. for how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I:On thepropagation of initial condition errors and their implications for data assimilation[J]. Mon Wea Rev, 2010, 138(1):242-255.
[23] FABRY F. For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part II:On theobservation signal from different sensors[J]. Mon Wea Rev, 2010, 138(1):256-264.
[24] GAO J, STENSRUD D J. Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeorclassification[J]. J Atmos Sci, 2011, 69(3):1 054-1 065.
[25] WANG H, SUN J, FAN S, et al. Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of foursummertime convective events[J]. Journal of Applied Meteorology&Climatology, 2013, 52(4):889-902.
[26] PARRISH D F,DERBER J C. The national meteorological center's spectral statistical-interpolation analysis system[J]. Mon Wea Rev,1992, 120(8):1 747-1 763.
[27] CHEN Y, XIA X, MIN J, et al. Balance characteristics of multivariate background error covariance for rainy and dry seasons and theirimpact on precipitation forecasts of two rainfall events[J]. Meteorology&Atmospheric Physics, 2016, 128(5):1-22.
[28]陈耀登,夏雪,闵锦忠,等.背景误差协方差变量平衡特征及其对台风同化和预报的影响[J].热带气象学报,2017,33(3):289-298.
[29] SUN J, WANG H, TONG W, et al. Comparison of the impacts of momentum control variables on high-resolution variational dataassimilation and precipitation forecasting[J]. Mon Wea Rev, 2016, 144(1):149-169.
[30] LIM E, SUN J. A Velocity dealiasing technique using rapidly updated analysis from a four-dimensional variational doppler radar dataassimilation system[J]. J Atmos Ocean Technol, 2010, 27(7):1 140-1 152.
[31] ROBERTS N M, LEAN H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J].Mon Wea Rev, 2008, 136(1):78-97.