Research progress and prospects of atmospheric motion vector based on meteorological satellite images
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摘要: 本文主要回顾了气象卫星导风的发展历程并对未来发展方向进行了阐述. 引言部分首先回顾发展历程并介绍了导风发展史上的一些里程碑事件，分别对中国、美国、欧洲以及日本的气象卫星导风情况进行了简要介绍. 第一节详细总结了多种传统气象卫星导风算法的特点和关键技术，介绍了交叉相关法、形态辨认法以及亚像元法. 此外，还描述了五种较为常用的高度指定算法，对传统导风追踪算法以及高度指定算法的原理进行了详细地描述. 第二节归纳了最近几年基于计算机视觉和机器学习技术发展起来的多种新体制的气象卫星导风产品，分别介绍了光流法、三维导风以及中尺度导风的优势和研究背景. 最后，对比了传统与新型气象卫星各种导风算法的优缺点，展望了未来的发展趋势和应用前景. 特别是指出光流法导风有着空间分辨率高、三维导风能得到更多层风场的信息、中尺度导风则能对特殊天气如热带气旋实现高时空分辨率的观测的优势，并提出三维导风与中尺度导风的应用研究将是未来重要发展方向.Abstract: 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.
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Key words:
- atmospheric motion vector /
- three-dimensional winds /
- tracking method /
- height assignment
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图 1 我国风云系列静止气象卫星大气导风产品的发展历程. 图片右上角数字为图像通道波长；红、绿、蓝三色分别代表高、中、低层的导风矢量
Figure 1. Development history of the AMV product of China's Fengyun geostationary series meteorological satellites. Number in the upper right corner of each picture indicates wavelength (μm); red, green, and blue vectors represent the wind vectors at the high, middle, and low layers of the atmosphere, respectively
图 2 交叉相关法获取气象卫星风矢量示意图
Figure 2. Diagram of retrieving wind vector by cross-correlation method
图 3 亚像元法示意图（修改自Bresky et al., 2012）
Figure 3. Schematic diagram of nested tracking method (modified from Bresky et al., 2012)
图 4 基于（a）交叉相关法与（b）光流法反演的风场（韩雷等，2008）
Figure 4. Wind fields retrieved by (a) cross-correlation and (b) optical flow methods (Han et al., 2008)
图 6 2002年美国堪萨斯州东北部上空的中尺度导风. 绿色、蓝色、紫色分别表示1000～700 hPa、700～400 hPa、400～100 hPa层内的矢量导风. 蓝色箭头突出了成熟对流附近的中对流层分流（Bedka and Mecikalski, 2005）
Figure 6. Mesoscale AMVs in northeast Kansas in 2002; green, blue, and purple AMV barbs represent AMVs at 1000-700 hPa, 700-400 hPa, and 400-100 hPa layers, respectively. Blue arrows highlight mid-tropospheric diffluence in the vicinity of the mature convection (Bedka and Mecikalski, 2005)
表 1 气象卫星导风反演方法对比
Table 1. Comparison of traditional and new AMV algorithms
方法 优点 缺点 传统导风方法 CC法 实现简单，精确，
空间分辨率较低耗费算力较大 模型识别法 适用范围广 精度不够高 亚像元法 降低慢速偏差，提高空间分辨率 耗费算力较大 新型导风方法 光流法 计算速度快，得出风场较平滑，能追踪风速大的矢量，分辨率高 耗费算力较大 三维导风 能得到多层导风，数据信息量巨大 时间频次较低，对红外高光谱探测器定标精度要求较高 中尺度导风 能够得到高时空分辨率导风产品 空间覆盖范围有限 
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