Algorithm for generating cloud‐mask from multi‐channel satellite data

Abstract

This article presents an algorithm for generating cloud‐mask, over the ocean, from multi‐channel satellite data. The algorithm is based on the assumption that, at any given instant of time, the observed radiances are a mixture of many Gaussians where each Gaussian mixture component is representative of a scene (clear or cloudy). The problem is attempted as one of the unsupervised clustering. The clusters in the data are separated using a Gaussian mixture model. The proposed algorithm is applied on INSAT‐3D imager data and its performance is assessed by comparing the cloud‐mask thus generated against the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud‐mask for forty selected days in the year 2014. The skill of the algorithm developed in this study is about 20% higher than the algorithm currently operational for generating the INSAT‐3D cloud‐mask. Due to the fact that currently geophysical parameter retrieval (e.g. sea‐surface temperature) and data assimilation are performed in clear‐sky regions, the developed algorithm will have large implications in retrieval and data assimilation studies.