Title
A modified RRSQRT-filter for assimilating data in atmospheric chemistry models
Author
Segers, A.J.
Heemink, A.W.
Verlaan, M.
van Loon, M.
Publication year
2000
Abstract
The RRSQRT-filter is a special formulation of the Kalman filter for assimilation of data in large scale models. In this formulation, the covariance matrix of the model state is expressed in a limited number of modes. Two modifications have been made to the filter such that it is more robust when applied in combination with an atmospheric chemistry model; both act on the reduction of the covariance matrix into modes. The first modification proposes a transformation of the state, which makes the reduction invariant for a change in units and helps to collect the most important covariance structures in the first modes. The second modification extracts additional information from the reduction algorithm to limit the formation of unphysical states by the filter. (C) 2000 Elsevier Science Ltd. The RRSQRT-filter is a special formulation of the Kalman filter for assimilation of data in large scale models. In this formulation, the covariance matrix of the model state is expressed in a limited number of modes. Two modifications have been made to the filter such that it is more robust when applied in combination with an atmospheric chemistry model; both act on the reduction of the covariance matrix into modes. The first modification proposes a transformation of the state, which makes the reduction invariant for a change in units and helps to collect the most important covariance structures in the first modes. The second modification extracts additional information from the reduction algorithm to limit the formation of unphysical states by the filter.
Subject
Atmospheric chemistry
Data assimilation
Kalman filter
Air pollution
Algorithms
Atmospheric chemistry
Computer simulation
Kalman filtering
Matrix algebra
Phase transitions
Data assimilation
Large scale models
Reduction algorithm
RRSQRT filter
State transformation
Mathematical models
atmospheric chemistry
data assimilation
filter
modeling
Kalman filter
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http://resolver.tudelft.nl/uuid:77642146-0fc6-4b21-bbc1-471b20efab4c
DOI
https://doi.org/10.1016/s1364-8152(00)00051-7
TNO identifier
235893
ISSN
1364-8152
Source
Environmental Modelling and Software, 15 (6-7 SPEC. ISS), 663-671
Document type
article