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