集合变换卡尔曼滤波局地化(LETKF)对区域集合初始扰动的影响
投稿时间: 2017-11-30  最后修改时间: 2018-03-03  点此下载全文
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作者单位E-mail
马旭林 南京信息工程大学大气科学学院 xulinma@nuist.edu.cn 
何佩仪 南京信息工程大学  
周勃旸 中国民用航空青岛空中交通管理站  
和杰 南京信息工程大学  
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目);国家重点基础研究发展计划(973计划)
中文摘要:集合变换卡尔曼滤波(ETKF)是一种有效的集合预报初始扰动方案,得到广泛应用。但是,ETKF方案中有限的集合样本、相同的集合成员设置以及预报模式误差等可能会使两个距离较远的状态变量产生较高的虚假相关,从而影响集合扰动的质量。为了有效解决远距离虚假相关的问题,将局地化思想引入ETKF方案。本文针对GRAPES区域集合预报系统(GRAPES REPS),对ETKF初值扰动局地化方案的效果进行了试验分析,为进一步改善和优化局地化方案提供依据。通过一周的连续试验,从暴雨个例、集合预报多种评分检验等方面分析了LETKF初始扰动方案所产生的集合预报质量。结果表明,区域集合预报中集合变换卡尔曼滤波初始扰动的局地化方案能够更加合理地捕捉到快速增长的分析误差的物理结构,更准确地再现数值模式的预报误差的线性与非线性传播和演变特征。该局地化方案可以较好地改进预报质量,提高降水预报的准确率,尤其是针对小雨、中雨、暴雨量级的预报。相对于现有区域集合预报的业务系统GRAPES REPS,基于局地化ETKF初始扰动方案的区域集合预报具有较明显的优势。总体来看,LETKF初始扰动方案确实可以更好地改善区域集合预报的质量。
中文关键词:集合预报,GRAPES,集合变换卡尔曼滤波,LETKF
 
The influence of the localization of ensemble transform Kalman filter (LETKF) on regional ensemble initial perturbations
Abstract:The finite ensemble sample size, the same ensemble member setting in ensemble transform Kalman filter(ETKF)and the forecast model error may make the two remote state variables have higher spurious correlation. The reason why ETKF generates spurious correlation is that each ensemble member is an estimate of the atmospheric state, while the degree of atmospheric freedom is too high, and the limited ensemble members are difficult to fully express. On the other hand, due to the error of the forecast model, the same size of members may lead to convergence of different ensemble members in the prediction process, resulting in spurious correlation. By means of localization, truncated the spurious correlation of error variance in the localized radius, thus solving the problem and improving the quality of error variance. That is to say, only the observation data in the local radius are absorbed aimed at a grid point, and the observation outside the radius is not taken into account so as to avoid the spurious correlation at a distance. In order to solve this problem, the localization of ETKF, called LETKF, is proposed. Based on the GRAPES regional ensemble prediction system(GRAPES_REPS), the local ETKF initial perturbation scheme is developed on the basis of the ETKF initial perturbation scheme, in order to improve the influence of the range spurious perturbation and the divergence of the filter in the regional ensemble prediction. Through a week of continuous experiments, we analyzed the ensemble prediction quality of LETKF initial perturbation scheme from the case of rainstorm, multiple scoring methods of ensemble prediction and the related structure and energy structure of initial perturbation. The results show that the scheme of local ensemble transform Kalman filter initial perturbation in regional ensemble prediction can be more reasonable to capture the fast-growing physical structure of analysis error, more accurately reappear the linear and nonlinear propagation and evolution characteristics of the forecast error in the numerical model. The localization scheme can improve the quality of the forecast and increase the accuracy of the precipitation forecast, especially for the forecast of the magnitude of the rain, the middle rain and the rainstorm. Compared with the ensemble prediction field of GRAPES_REPS business system, the ensemble prediction produced by LETKF initial perturbation scheme shows better prediction results in Multiple scoring methods. In general, the LETKF initial perturbation scheme improve exactly the quality of the regional ensemble prediction.
keywords:ensemble prediction, GRAPES, ETKF, LETKF
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