A novel approach to fog/low stratus detection using Meteosat 8 data

Abstract

A method is presented for fog and low stratus detection from daytime satellite imagery based on Meteosat 8 SEVIRI (Spinning-Enhanced Visible and Infra-Red Imager) data. With its excellent spatial, spectral and temporal resolutions, this imagery is an ideal basis for operational fog monitoring. The scheme utilizes a range of pixel-based and novel object-oriented techniques to separate fog and low stratus clouds from other cloud types. Fog and low stratus are identified by a number of tests which explicitly and implicitly address fog/low stratus spectral, spatial and microphysical properties. The scheme's performance is evaluated using ground-based measurements of cloud height over Europe. The algorithm is found to detect low clouds very accurately, with probabilities of detection (POD) ranging from 0.632 to 0.834 (for different inter-comparison approaches), and false alarm ratios (FAR) between 0.059 and 0.021. The retrieval of sub-pixel and temporal effects remain issues for further investigation.