[7] Multi-scale & multi-component treatments 2

[7-2] February 28, 11:40-12:00

Application of Meteorology-Chemistry Coupled Data Assimilation Using GEMS Synthetic Radiance Observations

E. Lee (Ewha Womans University), M. Zupanski (Colorado State University), and S. K. Park (Ewha Womans University)


Atmospheric chemistry and pollution interact with meteorological variables through dynamical and physical processes. For this reason, many studies on atmospheric chemical species and aerosols focus on the use of fully coupled meteorology-chemistry models in recent years. Concurrently, coupled data assimilation (DA) systems have been developed for better representation of coupled phenomena and balance between meteorology and chemistry. In this study, we develop a coupled DA system for WRF-Chem to assimilate observations from Geostationary Environmental Monitoring Spectrometer (GEMS) that is planned to be launched in 2019. We apply a hybrid ensemble-variational DA algorithm, called the Maximum Likelihood Ensemble Filter (MLEF), which generates error covariances in ensemble space and is appropriate for nonlinear observations. In MLEF, the interactions between meteorological and chemical variables occur through the cross error covariances. We assimilate both real meteorological observations and GEMS synthetic radiance observations to evaluate the coupled data assimilation system and to estimate the impact of observations. Here synthetic radiance observations are produced by high-resolution WRF-Chem simulations. We note that the coupled DA system, i.e., WRF-Chem-MLEF, successfully decreases forecast error, and shows the impact of meteorological variables to chemical variables, and vice versa; thus demonstrating interactions between meteorology and atmospheric chemistry.

  Presentation file: 07_2_E.Lee.pdf