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Being able to effectively identify clouds and monitor their evolution is oneimportant step toward more accurate quantitative precipitation estimation andforecast. In this study, a new gradient-based cloud-image segmentationtechnique is developed using tools from image processing techniques. Thismethod integrates morphological image gradient magnitudes to separable cloudsystems and patches boundaries. A varying scale-kernel is implemented to reducethe sensitivity of image segmentation to noise and capture objects with variousfinenesses of the edges in remote-sensing images. The proposed method isflexible and extendable from single- to multi-spectral imagery. Case studieswere carried out to validate the algorithm by applying the proposedsegmentation algorithm to synthetic radiances for channels of the GeostationaryOperational Environmental Satellites (GOES-R) simulated by a high-resolutionweather prediction model. The proposed method compares favorably with theexisting cloud-patch-based segmentation technique implemented in thePERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information usingArtificial Neural Network - Cloud Classification System) rainfall retrievalalgorithm. Evaluation of event-based images indicates that the proposedalgorithm has potential to improve rain detection and estimation skills with anaverage of more than 45% gain comparing to the segmentation technique used inPERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to98%.

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS