A simple sea fog prediction approach using GOCI observations and sea surface winds

Abstract

In this paper, we have proposed a method of sea fog prediction using model sea surface wind (SSW) data and hourly optical satellite data with 500 m spatial resolution. Three sea fog cases in the Yellow Sea were selected from Geostationary Ocean Color Imager (GOCI) images, and their shifts were determined from the fog cluster centroid displacements at successive hours. The 4 km resolution Weather Research and Forecasting (WRF) model SSW were used to predict sea fog shifts. The sea fog prediction results were verified using validation indices like probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The predictions started with high initial skill scores (POD = 0.76, FAR = 0.18, and CSI = 0.66), and showed values 0.59, 0.30, and 0.46 for POD, FAR, and CSI, respectively for predictions after two hours. After three hours their values still remained close to the medians though decreased, but afterwards decreased considerably for POD and CSI. Thus the method is found suitable for short-term prediction of sea fog. Additionally, trajectory sensitivities were compared between 20 and 4 km resolution WRF which, in general, resulted in less errors for 4 km WRF model SSW.