The Korea Meteorological Administration has employed the Asian Dust Aerosol Model 2 (ADAM2) to forecast Asian dust events since 2010, where the dust emission flux is proportional to the fourth power of the friction velocity. Currently, the dust emission reduction factor (RF) is determined by the normalized difference vegetation index (NDVI). This study aims to improve the forecasting capability of ADAM2 by developing a daily dust RF using both monthly (January 2007 to December 2016) and real-time moderate resolution imaging spectroradiometer (MODIS) NDVI data. We also developed a look-up table to transform the RF using NDVI and a system to update the RF by producing MODIS NDVI data for the last 30 days. Using these data, new RFs can be produced every day. To examine the impact of RF modification, the current (CTL) and new (EXP) RFs are compared during the period from March to May 2017. The simulations are verified by ground-based PM₁₀ observations from China and Korea. Accordingly, root mean square errors (RMSEs) are reduced by 11.58% when RF is updated using real-time NDVI data. The results suggest that recent daily NDVI data contribute positively to the forecasting ability of ADAM2, in the dust source and downwind regions.

The springtime dust events in Northeast Asia pose many economic, social, and health-related risks. Statistical models in the forecasting of seasonal dust events do not fully account for environmental variations in dust sources due to climate change. The Korea Meteorological Administration (KMA) recently developed the GloSea5-ADAM, a numerically based seasonal dust forecasting model, by incorporating the Asian Dust and Aerosol Model (ADAM)’s emission algorithm into Global Seasonal Forecasting Model version 5 (GloSea5). The performance of GloSea5 and GloSea5-ADAM in forecasting seasonal Asian dust events in source (China) and leeward (South Korea) regions was compared. The GloSea5-ADAM solved the limitations of GloSea5, which were mainly attributable to GloSea5′s low bare-soil fraction, and successfully simulated 2017 springtime dust emissions over Northeast Asia. The results show that GloSea5-ADAM’s 2017 and 2018 forecasts were consistent with surface PM₁₀ mass concentrations observed in China and South Korea, while there was a large gap in 2019. This study shows that the geographical distribution and physical properties of soil in dust source regions are important. The GloSea5-ADAM model is only a temporary solution and is limited in its applicability to Northeast Asia; therefore, a globally applicable dust emission algorithm that considers a wide variety of soil properties must be developed.

This study examined the surface-wetness effects in calculating dust generation in source regions, using Asian dust aerosol model version 3 (ADAM3; the control run; CNTL). Model sensitivity experiment was conducted in such a way that the dust generation in CNTL is compared against three ADAM3 versions with various surface-wetness effect schemes. The dust-generation algorithm in ADAM_RAIN utilizes precipitation, while the scheme in ADAM3_SM1 and ADAM3_SM2 employs soil water content to account for the surface-wetness effects on dust generation. Each run was evaluated for the spring (March–May) of 2020. ADAM3_SM1 shows the best performance for the dust source region in East Asia based on the root-mean-square error and the skill score, followed by ADAM3_SM2 and ADAM3_RAIN. Particularly, incorporation of the surface-wetness effects improves dust generation mostly in wet cases rather than dry cases. The three surface-wetness-effect runs reduce dust generation in the source regions compared to CNTL; hence, the inclusion of surface-wetness effects improves dust generation in the regions where CNTL overestimates dust generation.