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

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 20 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 25 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 30 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 Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-180, 2016 Manuscript under review for journal Geosci. Model Dev. Published: 21 July 2016 c © Author(s) 2016. CC-BY 3.0 License.

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