Computationally efficient air quality forecasting tool: implementation of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust
- 1Department of Earth and Atmospheric Sciences, University of Houston, 312 Science & Research Building 1, Houston, TX 77204, USA
- 2National Institute of Environmental Research, Incheon, Republic of Korea
- 3Division of Environmental Engineering, Ajou University, Suwon, Republic of Korea
- 4Department of Atmosphere Sciences, Yonsei University, Seoul, Republic of Korea
Abstract. This study suggests a new modeling framework using a hybrid Eulerian–Lagrangian-based modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for a prediction of an Asian dust event in Korea. The new version of STOPS (v1.5) has been implemented into the Community Multi-scale Air Quality (CMAQ) model version 5.0.2. The STOPS modeling system is a moving nest (Lagrangian approach) between the source and the receptor inside the host Eulerian CMAQ model. The proposed model generates simulation results that are relatively consistent with those of CMAQ but within a comparatively shorter computational time period. We find that standard CMAQ generally underestimates PM10 concentrations during the simulation period (February 2015) and fails to capture PM10 peaks during Asian dust events (22–24 February 2015). The underestimation in PM10 concentration is very likely due to missing dust emissions in CMAQ rather than incorrectly simulated meteorology, as the model meteorology agrees well with the observations. To improve the underestimated PM10 results from CMAQ, we used the STOPS model with constrained PM concentrations based on aerosol optical depth (AOD) data from the Geostationary Ocean Color Imager (GOCI), reflecting real-time initial and boundary conditions of dust particles near the Korean Peninsula. The simulated PM10 from the STOPS simulations were improved significantly and closely matched the surface observations. With additional verification of the capabilities of the methodology on emission estimations and more STOPS simulations for various time periods, the STOPS model could prove to be a useful tool not just for the predictions of Asian dust but also for other unexpected events such as wildfires and oil spills.