Global assessment of sand and dust storms

The specific objectives of the assessment are to: 1) Synthesise and highlight the environmental and socio-economic causes and impacts of SDS, as well as available technical measures for their mitigation, at the local, regional and global levels; 2) Show how the mitigation of SDS can yield multiple sustainable development benefits; 3) Synthesize information on current policy responses for mitigating SDS and 4) Present options for an improved strategy for mitigating SDS at the local, regional and global levels, building on existing institutions and agreements

Performance of KMA-ADAM3 in Identifying Asian Dust Days over Northern China

Recently, the Korea Meteorological Administration developed Asian Dust Aerosol Model version 3 (ADAM3) by incorporating additional parameters into ADAM2, including anthropogenic particulate matter (PM) emissions, modification of dust generation by considering real-time surface vegetation, and assimilations of surface PM observations and satellite-measured aerosol optical depth. This study evaluates the performance of ADAM3 in identifying Asian dust days over the dust source regions in Northern China and their variations according to regions and soil types by comparing its performance with ADAM2 (from January to June of 2017). In all regions the performance of ADAM3 was markedly improved, especially for Northwestern China, where the threat score (TS) and the probability of detection (POD) improved from 5.4% and 5.5% to 30.4% and 34.4%, respectively. ADAM3 outperforms ADAM2 for all soil types, especially for the sand-type soil for which TS and POD are improved from 39.2.0% and 50.7% to 48.9% and 68.2%, respectively. Despite these improvements in regions and surface soil types, Asian dust emission formulas in ADAM3 need improvement for the loess-type soils to modulate the overestimation of Asian dust events related to anthropogenic emissions in the Huabei Plain and Manchuria

Improved Dust Emission Reduction Factor in the ADAM2 Model Using Real-Time MODIS NDVI

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

Improved Dust Forecast by Assimilating MODIS IR-Based Nighttime AOT in the ADAM2 Model

A data assimilation (DA) system employing day- and nighttime aerosol optical thickness (AOT) was developed for the Asian Dust Aerosol Model 2 (ADAM2), using the optimal interpolation (OI) method. The DA system assimilated nighttime AOT for dust retrieved from MODIS infrared (IR) measurements with an artificial neural network (ANN) approach. An Asian dust case that occurred during 14-18 March 2009 was simulated using ADAM2. To examine the impact of the inclusion of nighttime AOT on forecasts of the data assimilation system, experiments were performed with different assimilation cycles (i.e., DA1: 24-hour cycle with daytime MODIS AOT only, DA2: 12-hour cycle with additional nighttime AOT). A control simulation was also performed without data assimilation (CTL). Forecasts were assessed using MODIS-derived AOT distributions as well as ground-based skyradiometer, PM10, and lidar observations. The model-estimated vertical distribution of the dust extinction coefficient was also compared with lidar measurements. Both experiments (DA1, DA2) were found to have improved forecasting, but DA2 outperformed DA1. Results suggest that the ANN-based nighttime AOT contributes more positively to the forecasting through better temporal coverage for data assimilation.OAIID:RECH_ACHV_DSTSH_NO:T201725518RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002329CITE_RATE:1.069DEPT_NM:지구환경과학부EMAIL:[email protected]_YN:YY