Articles | Volume 8, issue 5
Geosci. Model Dev., 8, 1315–1320, 2015
https://doi.org/10.5194/gmd-8-1315-2015
Geosci. Model Dev., 8, 1315–1320, 2015
https://doi.org/10.5194/gmd-8-1315-2015

Development and technical paper 05 May 2015

Development and technical paper | 05 May 2015

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

S. K. Park1,2,3,4, S. Lim2,3, and M. Zupanski5 S. K. Park et al.
  • 1Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 2Department of Atmospheric Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 3Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea
  • 4Severe Storm Research Center, Ewha Womans University, Seoul, Republic of Korea
  • 5Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA

Abstract. 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.

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Short summary
The structure of an ensemble-based coupled atmosphere-chemistry forecast error covariance is examined using the WRF-Chem, a coupled atmosphere-chemistry model. It is found that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. Additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert impact on chemistry analysis, and vice versa.