Ensemble‐based chemical data assimilation. II: Covariance localization

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air‐quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we implement, and assess the performance of, a localized ‘perturbed‐observations’ ensemble Kalman filter (LEnKF). We analyse different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach for chemical data assimilation. The results also point to several issues on which future research is necessary.