Sang-Sam Lee

PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

A data assimilation (DA) system using ground PM10 observation for Asian Dust Aerosol Model version 2 (ADAM2), which is the operational dust forecasting model of Korea Meteorological Administration (KMA), has been developed with the optimal interpolation (OI) method. The observations are provided by the PM10 network operated by KMA. Three DA experiments are performed to simulate a dust event observed in Korea from 1 March to 31 May 2009 with different assimilation cycles of 24 (DA24), 12 (DA12), and 06 hours (DA06). 48-hour forecasts from the adjusted Initial Condition (IC) of dust concentration are compared with control simulation (CTL) and observation from independent stations. It is found that CTL simulates spatial patterns of dust emitted and transported associated with a developing low pressure system over the dust source regions quite well, compared with satellite measurement. However, it appears that there is considerable uncertainty in estimating the concentration of dust. With IC adjustment, the model simulates improved dust concentration, showing considerably reduced RMSE, particularly for the prediction within 12 hours of forecast. At the same time, it is shown that the time interval of DA affects the predictability of ADAM2, so that DA06 appears to have better predictability within a 12-hour simulation, reducing RMSE by 50% compared with CTL. This suggests that assimilating PM10 to the dust prediction model using OI has the potential to predict air quality in Korea when the cycle of assimilation is sufficiently short.

Physical explanation of the weakened brightness temperature difference signal over the yellow sea during a dust event: Case study for March 15–16, 2009

This paper attempts to explain the cause of weakening or disappearing brightness temperature difference (BTD) signatures, in particular, over the Yellow Sea during the March 15–16, 2009 dust event. Using a simple correction approach that removes the effects of emissivity difference and water vapor effect difference, we confirmed that the weakening or disappearing BTD signatures noted over the Yellow Sea are largely due to the spectral emissivity contrast between land and ocean. The weakening or disappearing dust is hypothesized to be pronounced when the dust loading is weak because of the surface contribution to the top of atmosphere radiance, and that it is mainly due to the difference in spectral emissivity over the window band between land and ocean. It is further suggested that water vapor may be considered as a correction factor in spite of its smaller contribution.

Numerical simulation and evaluation of Asian dust events observed in Mongolia in spring 2011

The Asian dust forecasting model, Mongolian Asian Dust Aerosol Model (MGLADAM), has been operated by the National Agency for Meteorology and Environmental Monitoring of Mongolia since 2010, for the forecast of Asian dust storms. In order to evaluate the performance of the dust prediction model, we simulated Asian dust events for the period of spring 2011. Simulated features were compared with observations from two sites in the dust source region of the Gobi desert in Mongolia, and in the downstream region in Korea. It was found that the simulated wind speed and friction velocity showed a good correlation with observations at the Erdene site (one of the sites in the Gobi desert). The results show that the model is proficient in the simulation of dust concentrations that are within the same order of magnitude and have similar start and end times, compared with PM10 observed at two monitoring sites in the Gobi regions. Root Mean Square Error (RMSE) of the dust simulation ranges up to 200 μg m−3 because of the high concentrations in source regions, which is three times higher than that in the downstream region. However, the spatial pattern of dust concentration matches well with dust reports from synoptic observation. In the downwind regions, it was found that the model simluated all reported dust cases successfully. It was also found that the RMSE in the downwind region increased when the model integration time increased, but that in the source regions did not show consistent change. It suggests that MGLADAM has the potential to be used as an operational dust forecasting model for predicting major dust events over the dust source regions as well as predicting transported dust concentrations over the downstream region. However, it is thought that further improvement in the emission estimation is necessary, including accurate predictions in surface and boundary layer meteorology. In the downwind regions, background PM10 concentration is considerably affected by other aerosol species, suggesting that a consideration of anthropogenic pollutants will be required for accurate dust forecasting.