Abstract
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 to 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 hours 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, as well as a run with three-dimensional variational (3DVAR) DA. Consistent improvements due to the ICs assimilated with the EnKF were found in the DA experiments with 6 h interval, compared to the CTR run, and to the run with 3DVAR. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary concentrations (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 about 27.2 % for the 6 h reanalysis run. A series of 24 hour PM2.5 predictions were additionally conducted each day at 00 UTC with the optimized initial concentrations (ICs). The relative reduction of the NMB was 17.3 % for the 24 h prediction run, when the updated ICs were applied at 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.
Highlights
Among many air pollutants, particular attention has been paid to the issue of atmospheric aerosols in East Asia and South Korea, where large anthropogenic emissions from growing economic activities cause frequent high episodes of air pollution
The “analysis field” indicates the initial concentration fields updated by the ensemble Kalman filter (EnKF) method
395 To improve PM2.5 prediction in South Korea, we developed and applied an EnKF data assimilation method to the Weather Research and Forecasting (WRF)– Community Multiscale Air Quality (CMAQ) modeling system
Summary
Particular attention has been paid to the issue of atmospheric aerosols in East Asia and South Korea, where large anthropogenic emissions from growing economic activities cause frequent high episodes of air pollution. To achieve the goal of improving PM2.5 predictability, the National Institute of Environmental Research (NIER) of South Korea has implemented daily operational air quality forecast since 2014, using the 3-D Chemical Transport Model (CTM) (Chang et al, 2016), while the Korean Ministry of the 35 Environment (KMoE) provides real-time observations of PM2.5, together with the concentrations of five other criteria air pollutants (PM10, O3, CO, SO2, and NO2) in a website named “Air Korea” (https://www.airkorea.or.kr). The EnKF is relatively easy to implement without requiring a tangent linear or adjoint model, and can compute flow-dependent BEC from short-term ensemble predictions. This flow dependence of the BEC is one of the main reasons behind the possible success of the EnKF method, compared to other DA methods.
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