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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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 |
work_keys_str_mv |
AT skpark structureofforecasterrorcovarianceincoupledatmospherechemistrydataassimilation AT slim structureofforecasterrorcovarianceincoupledatmospherechemistrydataassimilation AT mzupanski structureofforecasterrorcovarianceincoupledatmospherechemistrydataassimilation |
_version_ |
1725907137681424384 |