An Online Assimilation Method to Improve the Numerical Forecast of Sea Fog Using Microwave Radiometer‐Retrieved Cloud Water Path

Abstract

AbstractNumerical forecast of the sea fog is sensitive to the initial moist stratification within the marine atmospheric boundary layer (MABL). This study develops an online assimilation method to improve the MABL thermal and moist structures in sea fog ensemble forecasts based on the Weather Research and Forecasting model and Grid‐point Statistical Interpolation/EnKF system. It uses the satellite‐retrieved cloud water path (CWP) as the indicator of sea fog and low‐level stratus to determine the best ensemble members at each grid point. The relative humidity and cloud water profiles are extracted from the best members to generate a series of pseudo‐observations, which are assimilated to update all the members by EnKF method. The new method significantly improves the ensemble forecast of five widespread advection fog events over the Yellow Sea, which can be attributed to the decrease of both missed and spurious fog areas. The case study shows that assimilating the information of both humidity and cloud water outperforms assimilating either of them, while the impact of directly assimilating CWP observation is insignificant. The analysis increments of cloud water, thermal and moist structures in MABL together contribute to the correction of forecasted sea fog. The generation of pseudo‐observations can use the dynamic compatibility of the model to alleviate the impact of erroneous data in the observation, leading to the low sensitivity of the new method to CWP retrieval error.