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Cloud motion winds (CMW) are routinely derived by tracking features insequential geostationary satellite infrared cloud imagery. In this paper, weexplore the cloud motion winds algorithm based on data-driven deep learningapproach, and different from conventional hand-craft feature tracking andcorrelation matching algorithms, we use deep learning model to automaticallylearn the motion feature representations and directly output the field of cloudmotion winds. In addition, we propose a novel large-scale cloud motion windsdataset (CMWD) for training deep learning models. We also try to use a singlecloud imagery to predict the cloud motion winds field in a fixed region, whichis impossible to achieve using traditional algorithms. The experimental resultsdemonstrate that our algorithm can predict the cloud motion winds fieldefficiently, and even with a single cloud imagery as input.

Generating the Cloud Motion Winds Field from Satellite Cloud Imagery Using Deep Learning Approach