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Algorithm for generating cloud-mask from multi-channel satellite data
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DOI：10.1002/qj.2738.png)
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Abstract
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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.
Keywords:
cloud-mask
unsupervised clustering
Gaussian mixture model
INSAT-3D
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