Ocean mesoscale eddy identification method and device

Abstract

The invention discloses an ocean mesoscale eddy identification method and a device. According to the method, firstly, an original data matrix of sea level anomaly data is established, and then the original data matrix is processed to obtain a variation field data matrix corresponding to the original data matrix. Secondly, the variation field data matrix is analyzed by adopting the trend surface analysis method, so that the universal Kriging interpolation calculation is conducted. In this way, a mesoscale eddy identification matrix capable of highly identifying mesoscale eddies is established. Compared with the prior art, based on the correlation between the pixels of the sea level height anomaly data matrix, the variation field data matrix for sea level height anomaly data is established. Meanwhile, the variation field data matrix is subjected to universal Kriging interpolation treatment. As a result, the identification accuracy and the identification efficiency of mesoscale eddies are improved.

Description

Technical field [0001] This application relates to the field of ocean remote sensing, and more specifically, to a method and device for identifying mesoscale vortices in the ocean. Background technique [0002] Meso-scale vortices are a kind of eddy currents in the ocean, which exist in all oceans. Compared with the large and stable circulation in the ocean, they are small-scale sea motions and are not significant, but mesoscale eddies play an important role in global ocean water distribution, energy and heat transfer, and affect the path and intensity of typhoons. Changes and even global climate change have a significant impact, so the efficient identification of mesoscale vortices has become a top priority. [0003] The existing high-efficiency meso-scale vortex identification mainly uses the closed contour method based on the sea level anomaly data matrix. In the traditional method, the correlation between the pixels in the sea level anomaly data matrix is ​​not deeply explored...

AI Technical Summary

Problems solved by technology

[0003] The existing high-efficiency mesoscale eddy identification mainly uses the closed contour method based on the sea level anomaly data matrix. In the traditional method, the correlation between each pixel in the sea level anomaly data matrix has not been deeply explored. , so the subsequent division and screening of closed contours are cumbersome and prone to misjudgment, resulting in low identification accuracy of mesoscale eddies

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