Alternative Approach for Satellite Cloud Classification: Edge Gradient Application

Global atmospheric heat exchanges are highly dependent on the variation of cloud types and amounts. For a better understanding of these exchanges, an appropriate cloud type classification method is necessary. The present study proposes an alternative approach to the often used cloud optical and thermodynamic properties based classifications. This approach relies on the application of edge detection techniques on cloud top temperature (CTT) derived from global satellite maps. The gradient map obtained through these techniques is then used to distinguish various types of clouds. The edge detection techniques used are based on the idea that a pixel's neighborhood contains information about its intensity. The variation of this intensity (gradient) offers the possibility to decompose the image into different cloud morphological features. High gradient areas would correspond to cumulus-like clouds, while low gradient areas would be associated with stratus-like clouds. Following the application of these principles, the results of the cloud classification obtained are evaluated against a common cloud classification method based on cloud optical properties' variations. Relatively good matches between the two approaches are obtained. The best results are observed with high gradient clouds and the worst with low gradient clouds.