Abstract

Most mesoscale eddy tracking methodologies used prior to this study evaluated eddy features using a distance-based proximity relationship, rather than considering similarities between eddies. This study applies a fast normalized cross-correlation methodology in the field of image registration to propose a novel mesoscale eddy tracking methodology that can rapidly and comprehensively calculate the similarities between two eddies and judge their relationship through the correlation coefficient, thus facilitating a more accurate mesoscale eddy trajectory tracking. The sea level anomaly data field is employed to identify the positions of eddies over time. The tracking methodology is then used to track the mesoscale eddy trajectories. After comparing the local nearest neighbor methodology (LNN) with our proposed new methodology in the Northwest Pacific Ocean, we conclude that the proposed methodology can address issues of discontinuity in tracking; especially in cases involving eddies with long lifespans. The tracking trajectories utilized in the proposed methodology achieve superior continuity and integrity and a higher degree of characterization than LNN, with the tracking results showing greater consistency with real eddy motion. The new methodology proposed in this paper has great significance for more widespread use.

摘要

在本研究之前使用的大多数中尺度涡流跟踪方法使用基于距离的邻近关系评估涡流特征，而不是考虑涡流之间的相似性。本研究应用图像配准领域的快速归一化互相关方法，提出一种新颖的中尺度涡流跟踪方法，可以快速全面地计算两个涡流之间的相似性，并通过相关系数判断它们之间的关系，从而促进更准确的中尺度涡流跟踪。涡流轨迹跟踪。海平面异常数据字段用于识别随时间变化的涡流位置。然后使用跟踪方法来跟踪中尺度涡流轨迹。在将局部最近邻方法 (LNN) 与我们在西北太平洋提出的新方法进行比较后，我们得出结论，所提出的方法可以解决跟踪不连续的问题；特别是在涉及长寿命涡流的情况下。所提出的方法中使用的跟踪轨迹实现了卓越的连续性和完整性以及比 LNN 更高程度的表征，跟踪结果显示与真实涡流运动的更大一致性。本文提出的新方法对于更广泛的使用具有重要意义。所提出的方法中使用的跟踪轨迹实现了卓越的连续性和完整性以及比 LNN 更高程度的表征，跟踪结果显示与真实涡流运动的更大一致性。本文提出的新方法对于更广泛的使用具有重要意义。所提出的方法中使用的跟踪轨迹实现了卓越的连续性和完整性以及比 LNN 更高程度的表征，跟踪结果显示与真实涡流运动的更大一致性。本文提出的新方法对于更广泛的使用具有重要意义。