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Liczba wyników
2014 | 2 | 587--595
Tytuł artykułu

An error estimate of Gaussian Recursive Filter in 3Dvar problem

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Computational kernel of the three-dimensional variational data assimilation (3D-Var) problem is a linear system, generally solved by means of an iterative method. The most costly part of each iterative step is a matrix-vector product with a very large covariance matrix having Gaussian correlation structure. This operation may be interpreted as a Gaussian convolution, that is a very expensive numerical kernel. Recursive Filters (RFs) are a well known way to approximate the Gaussian convolution and are intensively applied in the meteorology, in the oceanography and in forecast models. In this paper, we deal with an oceanographic 3D-Var data assimilation scheme, named OceanVar, where the linear system is solved by using the Conjugate Gradient (GC) method by replacing, at each step, the Gaussian convolution with RFs. Here we give theoretical issues on the discrete convolution approximation with a first order (1st-RF) and a third order (3rd-RF) recursive filters. Numerical experiments confirm given error bounds and show the benefits, in terms of accuracy and performance, of the 3-rd RF.(original abstract)
Rocznik
Tom
2
Strony
587--595
Opis fizyczny
Twórcy
  • University of Naples Federico II, Italy
  • Parthenope University of Naples, Italy
  • Parthenope University of Naples, Italy
  • Centro Euro-Mediterraneo sui Cambiamenti Climatici
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Bibliografia
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