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集合卡尔曼滤波同化中雷达位置的敏感性研究
引用本文:乔小湜,闵锦忠,王世璋.集合卡尔曼滤波同化中雷达位置的敏感性研究[J].Acta Meteorologica Sinica,2016,74(5):796-814.
作者姓名:乔小湜  闵锦忠  王世璋
作者单位:1.南京信息工程大学气象灾害教育部重点实验室, 南京, 210044
基金项目:国家重点基础研究发展计划973项目(2013CB430102)、国家自然科学基金青年基金(41505090)、北极阁基金(BJG201409)、南京信息工程大学人才启动经费(2014R007)、中国气象局武汉暴雨研究所暴雨开放基金项目(HR2008K01)。
摘    要:针对对流尺度集合卡尔曼滤波(EnKF)雷达资料同化中雷达位置对同化的影响进行研究。为了考察强对流出现在雷达不同方位时集合卡尔曼滤波同化雷达资料的能力,以一个理想风暴为例,设计了8个均匀分布在模拟区域周围的模拟雷达进行试验。单雷达同化试验中,初期同化对雷达位置较敏感,而十几个循环后对雷达方位的敏感性降低。造成初期同化效果较差的雷达观测位于模拟区域正南和正北方向,这两部雷达与模拟区域中心的连线垂直于风暴移动方向(即环境气流的方向)。双雷达试验的结果表明,正东、正南、正西和正北方向的雷达组合观测会使同化初期误差较大,这说明并不是所有与风暴连线成90°的雷达组合都能在短时同化中得到合理的分析结果,还需要都处于模拟区域对角线上(即与环境气流成45°夹角),同化效果才较好。短时同化后的确定性预报结果表明,较大分析误差也会导致较大预报误差。这些分析误差主要是由于同化初期不准确的集合平均场驱动出的不合理的背景误差协方差造成的。当背景场随着同化循环得到改进后,驱动出的合理的背景误差协方差使得不同位置雷达同化造成的差异逐步减小。基于上述结果,引入迭代集合均方根滤波(iEnSRF)算法,结果显示使用该算法后,雷达位置对同化效果的影响减小,同化不同位置的雷达资料均能有效降低分析和预报误差。 

关 键 词:资料同化    集合卡尔曼滤波    背景误差协方差    雷达位置
收稿时间:3/7/2016 12:00:00 AM
修稿时间:2016/5/17 0:00:00

A sensitivity study of radar location in data assimilation using the ensemble Kalman filter
QIAO Xiaoshi,MIN Jinzhong and WANG Shizhang.A sensitivity study of radar location in data assimilation using the ensemble Kalman filter[J].Acta Meteorologica Sinica,2016,74(5):796-814.
Authors:QIAO Xiaoshi  MIN Jinzhong and WANG Shizhang
Institution:1.Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China2.Shenyang Central Meteorological Observatory, Shenyang 110016, China
Abstract:The sensitivity of radar location in the ensemble Kalman filter (EnKF) data assimilation is studied. A suite of experiments has been conducted to simulate an idealized supercell storm case with assimilation of observations from radars located at various locations. There are eight radars used in these experiments. These radars are homogeneously placed around the simulated domain so that radar observations at all quadrants can be taken into account. Assimilations of observations from single radar and from two radars are investigated respectively. Results of the single radar data assimilation show that the radar location has obvious impacts on the first several cycles of EnKF data assimilation and these impacts become weak after more than ten cycles. Radars that can cause large analysis errors are found to be located at the north and south of the model domain. The lines connecting the center of the model domain and the locations of the two radars are orientated perpendicular to the moving direction of the storm (which is also the environmental flow direction). Results of the experiments with assimilation of observations from two radars show that the EnKF performs well when both radars are located at the diagonal of the simulated domain (the angle between the environmental flow and the lines connecting the radar locations and the storm center is 45°) and when the line between the domain center and one radar is orientated perpendicular to that between the domain center and the other radar. However, when both radars are located at the north, south, east or west of the simulated domain, the performance of EnKF with observations from two radars becomes worse in the early cycling stage. Results of the deterministic forecast after a short-term data assimilation show that the large analysis errors caused by radar location can further deteriorate the performance of subsequent forecasts. Those large analysis errors are caused by the poor error covariance driven by the inaccurate initial condition. With the improvement of background after more cycles, the covariance becomes better and the difference between experiments caused by using observations from different radars becomes smaller. Based on the above result, the iterative EnSRF (iEnSRF) is introduced. Results show that the performance of data assimilation in early cycles and the subsequent forecast are significantly improved when iEnSRF is employed.
Keywords:Data assimilation  Ensemble Kalman filter  Background error covariance  Radar location
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