Spectral Diagonal Ensemble Kalman Filters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00446922" target="_blank" >RIV/67985807:_____/15:00446922 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5194/npg-22-485-2015" target="_blank" >http://dx.doi.org/10.5194/npg-22-485-2015</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/npg-22-485-2015" target="_blank" >10.5194/npg-22-485-2015</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spectral Diagonal Ensemble Kalman Filters

Popis výsledku v původním jazyce

A new type of ensemble Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the approximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small ensembles and over multiple analysis cycles.

Název v anglickém jazyce

Spectral Diagonal Ensemble Kalman Filters

Popis výsledku anglicky

A new type of ensemble Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the approximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small ensembles and over multiple analysis cycles.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DG - Vědy o atmosféře, meteorologie

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-34856S" target="_blank" >GA13-34856S: Pokročilé metody náhodných polí v asimilaci dat pro krátkodobou předpověď počasí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nonlinear Processes in Geophysics

ISSN

1023-5809

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

13

Strana od-do

485-497

Kód UT WoS článku

000360655400010

EID výsledku v databázi Scopus

2-s2.0-84939635044