为了最终实现对海洋中尺度涡流(简称中尺度涡)的自动采样,首先应该发展中尺度涡动态特征识别技术。本文基于SLA(Sea Level Anomaly)数据,实现了对中尺度涡动态特征的检测算法。主要内容是制定了一个判别相邻两组SLA数据中的涡流,是否为同一涡流子在不同时刻的状态的标准,即判别下一时刻SLA数据中是否存在涡流是由上一时刻确定的被检测涡流演化而来的。通过确定这种进化关系,可以得到被检测涡流的一系列动态状态信息,例如:面积变化速率、中心移动情况以及其他情况。本算法的计算量不大,从而可以应用到实时涡流跟踪的环境中。值得注意的是,本文中的算法不仅仅局限于应用SLA数据,SSH(Sea Surface Height)等大部分反映海洋高度的数据也可以使用。
Abstract
To automatically sample mesoscale eddies by AUVs, the method to automatically recognize the dynamic features of eddies must be developed. In this study, a method for dynamic feature detection of mesoscale eddies was created based on SLA (Sea Level Anomaly) data. The main innovation is that a criterion to decide the succession relations of eddies was developed. With the succession relations of eddies being confirmed, we can calculate the area changing rate of eddy region, velocity of eddy centroid movement and some other dynamic features. Since the calculation cost of this algorithm is not enormous, it can be used in real time eddy tracking context, such as tracking eddies with AUVs. It is worth noting that SSH(Sea Surface Height) data can also be used in this algorithm. And SLA or SSH data can be acquired from the ocean numerical simulation model, satellite remote sensing or other methods. As long as SLA or SSH data are provided, our algorithm can be easily utilized for dynamic feature detection of mesoscale eddies.
关键词
中尺度涡 /
动态特征 /
演化关系 /
自动检测
Key words
mesoscale eddy /
dynamic feature /
succession relationship /
automatically detection
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基金
国家自然科学基金项目资助(61233013)
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