Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5

In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement in an existing system without seriously modifying the original CTM and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM2.5 observations at ground stations were assimilated in this DA system every 6 h over South Korea for the period of the KORUS-AQ campaign from 1 May to 12 June 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA and a run with three-dimensional variational (3D-Var) DA. Consistent improvements owing to the initial conditions (ICs) assimilated with the EnKF were found in the DA experiments at a 6 h interval compared to the CTR run and to the run with 3D-Var. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary conditions (BCs) on the PM2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be approximately 27.2 % for the 6 h reanalysis run. A series of 24 h PM2.5 predictions were additionally conducted each day at 00:00 UTC with the optimized ICs. The relative reduction of the NMB was 17.3 % for the 24 h prediction run when the updated ICs were applied at 00:00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM2.5 predictions in South Korea.

Automated summary

A data assimilation system using EnKF technique was developed to improve the simulations of a chemical transport model. Results showed consistent improvements in PM2.5 predictability through assimilating initial and boundary conditions, with relative reductions in prediction bias up to 27.2% and 17.3% for 6-hour and 24-hour predictions, respectively. Additional reductions in prediction bias (9.0%) were achieved for 24-hour PM2.5 predictions after updating boundary conditions from China.