Ensemble‐based chemical data assimilation. I: General approach

This is the authors' abstract. We don't add key points for this paper.

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 assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state‐of‐the‐art 4D‐Var approach. We analyse different aspects that affect the assimilation process, and investigate several ways to avoid filter divergence. Results with a real model and real observations show that EnKF is a promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary.