Premium
A regulated localization scheme for ensemble‐based Kalman filters
Author(s) -
Nerger Lars,
Janjić Tijana,
Schröter Jens,
Hiller Wolfgang
Publication year - 2011
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.945
Subject(s) - covariance , covariance intersection , weighting , kalman filter , covariance matrix , subcellular localization , mathematics , computer science , data assimilation , extended kalman filter , covariance function , algorithm , control theory (sociology) , artificial intelligence , statistics , physics , biochemistry , chemistry , control (management) , cytoplasm , meteorology , acoustics
Abstract Localization is an essential element of ensemble‐based Kalman filters in large‐scale systems. Two localization methods are commonly used: covariance localization and domain localization. The former applies a localizing weight to the forecast covariance matrix, while the latter splits the assimilation into local regions in which independent assimilation updates are performed. The domain localization is usually combined with observation localization, which is a weighting of the observation‐error covariance matrix, resulting in a similar localization effect to that of covariance localized filters. It is shown that the use of the same localization function in covariance localization and observation localization results in distinct effective localization length‐scales in the Kalman gain. In order to improve the performance of observation localization, a regulated localization scheme is introduced. Twin experiments with the Lorenz‐96 model demonstrate that the regulated localization can lead to a significant reduction of estimation errors as well as increased stability of the assimilation process. Copyright © 2011 Royal Meteorological Society