【作者】 朱琳；

【导师】 彭加毅； 寿绍文；

【作者基本信息】 南京信息工程大学 ， 气象学， 2007， 硕士

【摘要】 在大气海洋的科学研究中，随着卫星雷达等各种非常规资料迅速增加和模式的模拟能力加强，作为“把各种时空上不规则的零散分布的观测融合到基于物理规律的模式当中”的同化方法，在大气和海洋的研究中越来越重要。近几年来，一种新的同化方法—集合卡尔曼滤波同化方法受到广泛的关注和研究。现有的研究表明，集合卡尔曼滤波同化方法是一种具有业务应用潜力的同化方法。本文简单回顾了这种资料同化方法的发展历史和研究现状，介绍了其在气象中应用的基本原理。集合卡尔曼滤波的主要优点是利用随天气流型演变的背景场误差协方差来进行资料分析，这是在目前变分同化中难以实现的，也是变分同化中存在的主要问题之一。但是集合卡尔曼滤波也存在滤波发散，分析量不平衡等问题。本文利用浅水模式和实际大气预报模式(MM5)，通过一系列数值试验来对集合卡尔曼滤波的主要理论和方法进行了研究，包括集合卡尔曼滤波中随流型演变的背景场误差协方差，集合数对集合卡尔曼滤波的影响等，并和三维变分同化结果进行了初步比较和分析。得到以下结论：集合卡尔曼滤波同化的效果优于三维变分同化的效果，而且其误差是收敛的，造成差异的根本原因是三维变分无法实现更新误差协方差，而集合卡尔曼滤波的误差协方差是随流型演变的；随着集合数的增加，集合卡尔曼滤波同化的效果得到改善，主要原因是集合卡尔曼滤波的统计误差相关场存在虚假的相关，而集合数的增加会减少这种虚假的相关。更多还原

【Abstract】 "The blending of the existing noisy observation irregularly distributed in space and time into numerical models based on the physical laws that govern atmospheric flows became known as model assimilation of the data or data assimilation". It has become more and more important in the meteorology and oceanography because of the increasing observation and computation ability. In the recent years, a new data assimilation method called ensemble Kalman filter (EnKF) has aroused people’s attention. The available result indicates that EnKF owns the potential ability of becoming an operational data assimilation method. This article simply reviews the developmental history and study actuality of this advanced data assimilation method.The main virtue of the EnKF is that its background error covariance is flow-dependent. But the flow-dependent background error covariance is not easy to obtain in the operational variation analysis at present. But the EnKF aIso has drawbacks, for example, filter divergence, unbalance between the analysis variables et al. A serial experiment using the shallow water equation and the real atmosphere model (MM5) were carried out in order to study the EnKF theory, including the flow-dependent background error covariance in EnKF, the ensemble number’s effect on the EnKF system and the comparison between EnKF and 3D-VAR. From the experiments it is seen that: the EnKF is superior to the 3D-VAR and its error is convergent. The main reason is that the 3D-VAR can’t update its background error variance but the EnKF’s background error covariance is flow-dependent. As the number of ensemble increasing the EnKF’s analysis quality is improved because the increasing ensemble numbers will reduce the spurious error correlations.更多还原