| 迭代EnSRF方案设计及在Lorenz96模式下的检验 |
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| 引用本文： | 闵锦忠,王世璋,陈杰,杨春.迭代EnSRF方案设计及在Lorenz96模式下的检验[J].大气科学,2012,36(5):889-900. |
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| 作者姓名： | 闵锦忠 王世璋 陈杰 杨春 |
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| 作者单位： | 1.南京信息工程大学气象灾害省部共建教育部重点实验室,南京 210044;南京信息工程大学大气科学学院,南京 210044 |
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| 基金项目： | 公益性行业专项GYHY200806029,科技创新工程重大项目培育基金708051,国家自然科学基金资助项目40975068,江苏高校优势学科建设工程资助项目 (PAPD) |
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| 摘 要： | 本文利用非同步 (Asynchronous) 算法设计了一个包含迭代过程的集合平方根滤波方案 (迭代EnSRF),并在Lorenz96模式下详细对比分析了该方案和传统EnSRF方案的同化效果.与传统EnSRF方案不同,迭代EnSRF方案能够同时更新两个时次的背景场并通过迭代过程来改进分析结果.本文不仅检验了迭代EnSRF在同化不同类型观测资料时的效果,还检验了存在模式误差时该方案的同化效果,并且对同化结果的合理性进行了详细分析.试验结果表明:在完美模式下,迭代EnSRF能够显著加快同化常规观测时的收敛速度,并能够更加有效地同化非常规观测资料;在存在模式误差时,迭代EnSRF并不能有效改进分析结果;当对不准确的模式参数进行扰动后,迭代EnSRF能够更好地利用改进后的集合预报系统来提高其对部分类型观测的分析结果.进一步的分析表明,分析结果的改进主要得益于迭代EnSRF改进了背景误差协方差空间结构,并使得EnSRF的线性假设得到更好的满足.
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| 关 键 词： | 迭代EnSRF Lorenz96 背景误差协方差 模式误差 |
| 收稿时间： | 2011/10/9 0:00:00 |
| 修稿时间： | 2012/4/27 0:00:00 |
The Implementation and Test of Iterative EnSRF with Lorenz96 Model |
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| MIN Jinzhong,WANG Shizhang,CHEN Jie and YANG Chun.The Implementation and Test of Iterative EnSRF with Lorenz96 Model[J].Chinese Journal of Atmospheric Sciences,2012,36(5):889-900. |
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| Authors: | MIN Jinzhong WANG Shizhang CHEN Jie and YANG Chun |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science&Technology, Nanjing 210044;College of Atmospheric Science, Nanjing University of Information Science&Technology, Nanjing 210044;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science&Technology, Nanjing 210044;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science&Technology, Nanjing 210044;Unit 94701, PLA, Anqing 246001;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science&Technology, Nanjing 210044 |
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| Abstract: | A variant of Ensemble Square Root Filter (EnSRF) referred as iterative EnSRF is designed according to asynchronous algorithm. The performance of iterative EnSRF is examined using Lorenz96 model. Unlike traditional EnSRF, the iterative EnSRF can synchronously update two model states at different time and improve the analysis by iterative procedure. The performance of iterative EnSRF is examined not only by using different kinds of observations but also by using perfect and imperfect models. Meanwhile, the rationality of iterative EnSRF analysis is also discussed. With a perfect model, iterative EnSRF is able to increase the convergence speed of regular data assimilation and analyze the indirect observation more effectively. With an imperfect model, iterative EnSRF cannot effectively improve the analysis for all tested observations. If the incorrect parameter is perturbed, iterative EnSRF is able to utilize the improvement of ensemble forecast system to optimize the analysis for parts of observations. Further investigation of experiment results indicates that the improvement of iterative EnSRF analysis is contributed to the optimization of spatial structure of background error covariance and the linear assumption of EnSRF being more reasonable in iterative EnSRF procedure. |
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| Keywords: | iterative EnSRF Lorenz96 background error covariance model error |
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