کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5002312 1368452 2016 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Reduced Order Gaussian Smoothing for Nonlinear Data Assimilation
ترجمه فارسی عنوان
کاهش ضریب همبستگی گاوسی برای جمع آوری داده های غیرخطی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مکانیک محاسباتی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: IFAC-PapersOnLine - Volume 49, Issue 18, 2016, Pages 199-204
نویسندگان
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