  Cloud motion winds (CMW) are routinely derived by tracking features in
sequential geostationary satellite infrared cloud imagery. In this paper, we
explore the cloud motion winds algorithm based on data-driven deep learning
approach, and different from conventional hand-craft feature tracking and
correlation matching algorithms, we use deep learning model to automatically
learn the motion feature representations and directly output the field of cloud
motion winds. In addition, we propose a novel large-scale cloud motion winds
dataset (CMWD) for training deep learning models. We also try to use a single
cloud imagery to predict the cloud motion winds field in a fixed region, which
is impossible to achieve using traditional algorithms. The experimental results
demonstrate that our algorithm can predict the cloud motion winds field
efficiently, and even with a single cloud imagery as input.
