Research progress and prospects of atmospheric motion vector based on meteorological satellite images

This paper mainly reviews the history and prospects of the atmospheric motion vector (AMV) of meteorological satellites. The development history of AMV and some milestone events are first introduced before briefly discussing them in the contexts of China, the United States, Europe, and Japan. The first section provides a detailed summary of the characteristics and key technologies of various traditional AMV algorithms, introduces the cross-correlation, pattern recognition, and nested tracking approaches, and describes five commonly used height assignment algorithms and their basic principles. The second section discusses several recently developed AMV products based on computer vision and machine learning technologies and introduces the advantages and research histories of the optical flow method, and three-dimensional and mesoscale AMVs. Finally, we compare the advantages and disadvantages of new and traditional AMV algorithms before examining the potential for future applications and development trends. We specifically highlight the higher spatial resolution obtained by the advanced optical flow method, better wind field information from three-dimensional AMV, and finer spatial and temporal resolutions of special weather from mesoscale AMV such as cyclones. Furthermore, we predict more promising three-dimensional and mesoscale AMVs in the upcoming future.