Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation

In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control...

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Main Authors: S. K. Park, S. Lim, M. Zupanski
Format: Article
Language:English
Published: Copernicus Publications 2015-05-01
Series:Geoscientific Model Development
Online Access:http://www.geosci-model-dev.net/8/1315/2015/gmd-8-1315-2015.pdf
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spelling doaj-b9e7ddb93f3c4bfbaa9c3086477519ac2020-11-24T21:45:01ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032015-05-01851315132010.5194/gmd-8-1315-2015Structure of forecast error covariance in coupled atmosphere–chemistry data assimilationS. K. Park0S. Lim1M. Zupanski2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of KoreaDepartment of Atmospheric Science and Engineering, Ewha Womans University, Seoul, Republic of KoreaCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USAIn this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.http://www.geosci-model-dev.net/8/1315/2015/gmd-8-1315-2015.pdf
collection DOAJ
language English
format Article
sources DOAJ
author S. K. Park
S. Lim
M. Zupanski
spellingShingle S. K. Park
S. Lim
M. Zupanski
Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
Geoscientific Model Development
author_facet S. K. Park
S. Lim
M. Zupanski
author_sort S. K. Park
title Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
title_short Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
title_full Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
title_fullStr Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
title_full_unstemmed Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
title_sort structure of forecast error covariance in coupled atmosphere–chemistry data assimilation
publisher Copernicus Publications
series Geoscientific Model Development
issn 1991-959X
1991-9603
publishDate 2015-05-01
description In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.
url http://www.geosci-model-dev.net/8/1315/2015/gmd-8-1315-2015.pdf
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AT mzupanski structureofforecasterrorcovarianceincoupledatmospherechemistrydataassimilation
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