文章基本信息

标题：Reduced Order Gaussian Smoothing for Nonlinear Data Assimilation
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作者：Sarah A. King ; Sarah A. King ; Wei Kang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：18
页码：199-204
DOI：10.1016/j.ifacol.2016.10.163
语种：English
出版社：Elsevier
摘要：Abstract: We investigate Gaussian filtering for data assimilation in numerical weather prediction (NWP). Data assimilation is the process of combining prior forecasts and observations to produce a system estimate. The prevailing data assimilation method in operational NWP centers is variational data assimilation. This method involves solving a cost function over a time window forming a maximum likelihood estimate. This method, however, requires the use of linearized models which in practice are difficult to produce and maintain. As an alternative we propose Gaussian smoothing for derivative-free, nonlinear data assimilation. Gaussian filters and their corresponding smoothers use numerical integration to evaluate the recursive Bayesian formulas for optimal filtering under Gaussian assumptions. This numerical integration typically requires many model evaluations making conventional Gaussian filtering/smoothing impractical for use in NWP. We will present a reduced order method for forming a Rauch-Tung-Striebel (RTS) type smoother. To do so we review the Bayesian filtering and smoothing equations and discuss an efficient numerical method for evaluating them. We will then discuss a numerical example using the Korteweg-de Vries equation to compare our technique to the standard variational approach.
关键词：KeywordsState EstimationLarge-Scale SystemsOrder ReductionSmoothingFiltering