A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

Fog is a hazardous weather event that can endanger navigation, aviation, and transportation. While human has several limitations in detecting forecasting offshore fog, satellite remote sensing offers cost-effective images. In this study, probability-based daytime sea fog detection algorithm, applied to geostationary operational environmental (GOES) 16 data over the Grand Banks Eastern Canada, presented compared with National Oceanographic Atmospheric Administration (NOAA)'s Low Instrument Flight Rules (LIFR) probability map. Initially, clear-sky ice cloud classes were delineated GOES-16 image then remaining pixels assigned by conducting small droplet proxy, spatial homogeneity, temperature difference tests. Moreover, green band was linearly interpolated using first three bands of images generate pseudotrue color composites. The resulting maps evaluated both during an extended statistical measures. average for observed advection events 66% proposed method, while NOAA's LIFR map 38%. Furthermore, thresholding generated at 60%, false alarm rate, detection, hit Hanssen-Kuiper skill score 0.09, 0.77, 0.83, 0.68, respectively. method operationally being used region detect monitor facilitating safe navigation aviation. This study uses discusses satellite-based solution modeling Banks, NL.