Abstract. This study suggests a new modeling framework using a hybrid Lagrangian-Eulerian based modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for a more accurate prediction of 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. We apply STOPS to PM10 simulations in the East Asia during Asian dust events (22–24 February, 2015). The STOPS modeling system is a moving nest (Lagrangian approach) between the source and the receptor inside a CMAQ structure (Eulerian model). The proposed model generates simulation results that are relatively consistent with those of CMAQ but within a comparatively shorter computational time period. We evaluate the performance of standard CMAQ for the PM10 simulations and investigate the impact of STOPS modeling with constrained PM concentration based on space-derived measurement (by using alternative PM emissions) on the improved accuracy of the PM10 prediction. 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. Accurately simulated meteorology implies that the underestimated PM10 concentration is not due to the meteorology but to poorly estimated dust emissions for the CMAQ simulation. To improve the underestimated PM10 results from standard CMAQ, we use the STOPS modeling system inside of the CMAQ model, and instead of running the costly, time-consuming Eulerian model, CMAQ, we run several STOPS simulations using constrained PM concentration based on aerosol optical depth (AOD) data from Geostationary Ocean Color Imager (GOCI), reflecting real-time initial and boundary conditions of dust particles near the Korean Peninsula. The STOPS simulations with constrained PM concentration by GOCI-derived AOD show a significant increase in simulated PM10 compared to standard CMAQ. Moreover, the STOPS results were closely matched to surface data. These promising results imply that STOPS could prove to be a useful tool for more accurately predicting Asian dust events in Korea. With additional verification of the capabilities of the methodology on concentration estimations and more STOPS simulations for various time periods, the benefit of STOPS modeling for more accurate predictions of Asian dust could be generalized to the simulation and forecasting of unexpected events such as wildfires and upset emissions events in industrial regions over the East Asia.
Supplementary material to "The new implementation of a computationally efficient modeling tool (STOPS v1.5) into CMAQ v5.0.2 and its application for a more accurate prediction of Asian dust"
