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Automatically Locate Tropical Cyclone Centers Using Top Cloud Motion Data Derived From Geostationary Satellite Images

机译:使用对地静止卫星图像得出的顶级云运动数据自动定位热带气旋中心

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This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies.
机译:本文介绍了一种新技术,该技术可根据连续对地静止卫星图像中的顶云运动自动定位热带气旋(TC)中心。高分4号对地静止卫星的高成像速率和空间分辨率图像使我们能够得出TC的像素级顶云运动数据,并且根据这些数据,可以基于与基于TC的完全不同的原理准确地确定TC螺旋中心。静态图片功能。首先,提出了一种物理运动场分解方法,以消除运动数据中的场景偏移和TC迁移,而无需任何辅助地理位置数据。此分解不会生成出现在先前发布的运动场分解结果中的伪像。然后,对嵌入金字塔搜索结构中基于运动方向的索引的算法进行了全面设计,以自动有效地定位TC中心。测试表明,在提议的运动场分解之后,TC同心运动更清晰地显示出来,并且所定位的中心在视觉上与云模式中心以及四个气象机构的最佳航迹数据集高度吻合。

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