A System of Data Assimilation Based on Parallel Second Order Adjoint and Reduced Rank Kalman-Filter Methods

基于并行二阶伴随和降阶卡尔曼滤波方法的数据同化系统

• 批准号: 0201808
• 负责人: Ionel Navon
• 金额: --
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-15 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0201808&HistoricalAwards=false
• 关键词: System; Data; Assimilation; Based; Parallel

Four-dimensional variational analysis (4-D Var) is the most advanced data assimilation system currently employed in operational numerical weather prediction centers. While the scheme has many strengths and advantages, one shortcoming is that the error covariance matrix is fixed. The Kalman filter (KF) approach alleviates this shortcoming by explicitly propagating the error covariance matrix for model variables so that it is flow dependent. However, the computer memory requirement for the KF approach is very large, rendering it unfeasible in operational applications. To address this problem, this project will study the feasibility of developing a reduced rank Kalman filter to be coupled with the 4-D Var system. Namely, the 4-D Var scheme will be used to perform the analysis but the background cost function will use a flow-dependent error covariance matrix for the subspace defined by the leading Hessian singular vectors. The research will be built on an advanced numerical weather prediction model for which the second order adjoint system will be developed. A comparison of the reduced rank Kalman filter with the ensemble Kalman filter (EnKF) will be made using the ECMWF analysis data and satellite observations. 4-D Var remains the leading-edge technology for data assimilation of NWP systems. This research has the potential to improve 4-D Var applications in the operational environment.

四维变分分析（4-D Var）是目前数值天气预报业务中心采用的最先进的资料同化系统。 虽然该方案具有许多优点和优势，但一个缺点是误差协方差矩阵是固定的。 卡尔曼滤波器（KF）的方法通过显式传播模型变量的误差协方差矩阵，使它是流量相关的，从而弥补了这一缺点。 然而，KF方法的计算机存储器需求非常大，使得其在操作应用中不可行。 为了解决这个问题，本项目将研究开发一个降阶卡尔曼滤波器耦合到4-D无功系统的可行性。 也就是说，4-D Var方案将用于执行分析，但背景成本函数将使用由前导Hessian奇异向量定义的子空间的流量相关误差协方差矩阵。 该研究将建立在先进的数值天气预报模型基础上，并将开发二阶伴随系统。 将使用ECMWF分析数据和卫星观测对降秩卡尔曼滤波器与集合卡尔曼滤波器（EnKF）进行比较。 四维变分仍然是数值预报系统资料同化的前沿技术。 这项研究有可能提高4-D VAR的应用在操作环境中。