Effect of Observation Network Design on Meteorological Forecasts of Asian Dust Events

Abstract

o improve the prediction of Asian dust events on the Korean Peninsula, meteorological fields must be accurately predicted because dust transport models require them as input. Accurate meteorological forecasts could be obtained by integrating accurate initial conditions obtained from data assimilation processes in numerical weather prediction. In data assimilation, selecting the appropriate observation location is important to ensure that the initial conditions represent the surrounding meteorological flow. To investigate the effect of observation network configuration on meteorological forecasts during Asian dust events on the Korean Peninsula, observing system simulation experiments using several simulated and real observation networks were tested with the Weather Research and Forecasting modeling system for 11 Asian dust events affecting the Korean Peninsula during a recent 6-yr period. First, the characteristics of randomly fixed and adaptively selected observation networks were investigated with various observation densities. The adaptive observation strategy could reduce forecast errors more efficiently than the fixed observation strategy. For both the fixed and adaptive observation strategies, the mean forecast error reduction rates increased as the number of assimilated observations and the distance between observation sites increased up to 300 km. Second, the effects of redistributing the real observation sites and adding observation sites to the real observation network based on the adaptive observation strategy were investigated. Adding adaptive observation sites to the real observation network in statistically sensitive regions improved the forecast performance more than redistributing real observation sites did. The strategy of adding adaptive observation sites is used to suggest the optimal meteorological observation network for meteorological forecasts of Asian dust transport events on the Korean Peninsula.