Asian dust storms occur often and have a great impact on East Asia and the western Pacific in spring. Early warnings based on reliable forecasts of dust storms thus are crucial for protecting human health and industry. Here we explore the efficacy of 4-D variational method-based data assimilation in a chemical transport model for dust storm forecasts in East Asia. We use a 3-D global chemical transport model (GEOS-Chem) and its adjoint model with surface PM10 mass concentration observations. We evaluate the model for several severe dust storm events, which occurred in May 2007 and March 2011 in East Asia. First of all, simulated the PM10 mass concentrations with the forward model showed large discrepancies compared with PM10 mass concentrations observed in China, Korea, and Japan, implying large uncertainties of simulated dust emission fluxes in the source regions. Based on our adjoint model constrained by observations for the whole period of each event, the reproduction of the spatial and temporal distributions of observations over East Asia was substantially improved (regression slopes from 0.15 to 2.81 to 0.85-1.02 and normalized mean biases from -74%-151% to -34%-1%). We then examine the efficacy of the data assimilation system for daily dust storm forecasts based on the adjoint model including previous day observations to update the initial condition of the forward model simulation for the next day. The forecast results successfully captured the spatial and temporal variations of ground-based observations in downwind regions, indicating that the data assimilation system with ground-based observations effectively forecasts dust storms, especially in downwind regions. However, the efficacy is limited in nearby the dust source regions, including Mongolia and North China, due to the lack of observations for constraining the model.
Keywords: Adjoint model; Asian dust; Chemical transport model; Dust forecast.
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