Hybrid ETKF–3DVAR方法同化多普勒雷达速度观测资料I.模拟资料试验
投稿时间: 2014-02-10  最后修改时间: 2014-02-18  点此下载全文
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作者单位E-mail
沈菲菲 南京信息工程大学 ffshen.nuist@gmail.com 
闵锦忠 南京信息工程大学  
许冬梅 南京信息工程大学  
中文摘要:本文利用WRF模式及模式模拟的资料,开展了利用Hybrid ETKF–3DVAR方法同化模拟雷达径向速度资料的试验。该混合同化方法将集合转换卡尔曼滤波得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把“流依赖”的集合协方差信息引入到变分目标函数中去,在三维变分(three dimensional variational analysis, 3DVAR)框架基础下与观测数据进行糅合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF–3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置相比3DVAR更加接近“真实场”,对台风路径预报也有明显改进。通过对比HybridS试验与HybridF试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的“流依赖”信息,集合平均场代替确定性背景场带来的效果并不显著。
中文关键词:Hybrid ETKF–3DVAR  WRF模式  多普勒雷达资料
 
Assimilation of Doppler Radar Velocity Observations with Hybrid ETKF–3DVAR Method Part I: Simulated Data Experiments
Abstract:The Hybrid ETKF–3DVAR method is used to assimilate simulated Doppler radial velocity observations. The hybrid scheme updated the ensemble mean using a hybrid ensemble and static background-error covariance on the basis of 3DVAR framework. The ensemble perturbations in the hybrid scheme are updated by the ETKF scheme, which updates the background perturbation through a transform matrix. The results show that Hybrid ETKF–3DVAR may provide more accurate analysis than traditional 3DVAR. Additionally, significant positive impact from the hybrid data assimilation is found in vortex structure and position as well as the track forecast.It is found that such positive improvements were mostly provided by the flow-dependent covariance other than the use of ensemble mean by comparing the results from 3DVAR and the HybridS experiment, which uses static background-error covariance and ensemble mean as the first guess.
keywords:Hybrid ETKF–3DVAR,WRF model,Doppler radar data
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