Numerical simulations of Asian dust events: A Lagrangian Dust Model and its applications

Abstract

An uni-modal Lagrangian Dust Model (LDM) was developed to simulate the dust concentrations and source-receptor (SR) relationships for recent Asian dust events that occurred over the Korean Peninsula. The following dust sources were used for the S-R calculation in this study: S-I) Gurbantunggut desert, S-II) Taklamakan desert, S-III) Tibetan Plateau, S-IV) Mu Us Desert, S-V) Manchuria, and S-VI) Nei Mongol and Gobi Desert. The following two 8-day dust simulation periods were selected for two case studies: (Period A) March 15–22, 2011, and (Period B) April 27–May 4, 2011. During two periods there were highly dense dust onsets observed over a wide area in Korea. Meteorological fields were generated using the WRF (Weather Research and Forecasting) meteorological model, and Lagrangian turbulent properties and dust emission were estimated using FLEXPART model and ADAM2 (Asian Dust Aerosol Model 2), respectively. The simulated dust concentrations are compared with point measurements and Eulerian model outputs. Statistical techniques were also employed to determine the accuracy and uncertainty associated with the model results. The results showed that the LDM compared favorably well with observations for some sites; however, for most sites the model overestimated the observations. Analysis of S-R relationships showed that 38–50% of dust particles originated from Nei Mongol and the Gobi Desert, and 16–25% of dust particles originated from Manchuria, accounting for most of the dust particles in Korea. Because there is no nudging or other artificial forcing included in the LDM, higher error indicators (e.g., root mean square error, absolute gross error) were found for some sites. However, the LDM was able to satisfactorily simulate the maximum timing and starting time of dust events for most sites. Compared with the Eulerian model, ADAM2, the results of LDM found pattern correlations (PCs) equal to 0.78-0.83 and indices of agreement (IOAs) greater than 0.6, suggesting that LDM is capable of estimation of dust concentrations with the quantitative information on the S-R relationships that can be easily obtained by LDM.