Abstract

Fog that refers to the concentration of ice or water droplets in the near surface air is an important short-time meteorological phenomenon. As the measure of visibility of air environment, it directly affects societal economic activities and daily lives. As more and more high-frequency observatio... Show moreFog that refers to the concentration of ice or water droplets in the near surface air is an important short-time meteorological phenomenon. As the measure of visibility of air environment, it directly affects societal economic activities and daily lives. As more and more high-frequency observations (observations with short time intervals) become available, understanding how to make full use of such observed data to improve fog forecasting is an important and urgent research topic. Based on the Weather Research and Forecasting (WRF) Model and an observation simulation system experiment (OSSE) framework, this study explores a modified three-dimensional variational (3D-Var) data assimilation (DA) scheme to address the utilization of high-frequency observations on fog forecasting. In the modified 3D-Var scheme, the large-scale analysis constraint (LSAC) method is employed to the WRF 3D-Var. A dense fog event, which occurred in the North of China in 2007, is selected for the case study. Experimental results show that coherently combining high-frequency observational information with large-scale analysis information enables to significantly improve the 3D-Var analyses and the initialized model forecasts of fog coverage, especially over areas with coarse observations. The modified scheme is therefore promising for improving the routine forecasting of coastal sea fog. The optimal DA interval for fog forecasting is also discussed in this study. Show less