Preprints
https://doi.org/10.5194/essd-2023-138
https://doi.org/10.5194/essd-2023-138
21 Apr 2023
 | 21 Apr 2023
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll–a Data based on Deep Learning

Yan Wang, Jie Yang, Kai Wu, Meng Hou, and Ge Chen

Abstract. This paper presents an observational dataset on submesoscale eddies, which obtains from high–resolution chlorophyll–a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7–X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to understanding marine environments and ecosystems, as well as improving climate model predictions. The dataset is available at https://doi.org/10.5281/zenodo.7694115 (Wang and Yang, 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yan Wang, Jie Yang, Kai Wu, Meng Hou, and Ge Chen

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-138', Anonymous Referee #1, 28 May 2023
    • AC1: 'Reply on RC1', Yan Wang, 28 May 2023
  • RC2: 'Comment on essd-2023-138', Anonymous Referee #2, 15 Aug 2023
    • AC2: 'Reply on RC2', Yan Wang, 19 Aug 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-138', Anonymous Referee #1, 28 May 2023
    • AC1: 'Reply on RC1', Yan Wang, 28 May 2023
  • RC2: 'Comment on essd-2023-138', Anonymous Referee #2, 15 Aug 2023
    • AC2: 'Reply on RC2', Yan Wang, 19 Aug 2023
Yan Wang, Jie Yang, Kai Wu, Meng Hou, and Ge Chen

Data sets

Identification of Submesoscale Eddy Datasets Using AI Methods from GOCI I Chlorophyll Yan Wang https://doi.org/10.5281/zenodo.7694115

Video supplement

Submesoscale eddy variations on an hourly time scale Yan Wang https://youtube.com/shorts/ZtZWRXOYDiQ?feature=share

Yan Wang, Jie Yang, Kai Wu, Meng Hou, and Ge Chen

Viewed

Total article views: 865 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
606 218 41 865 33 38
  • HTML: 606
  • PDF: 218
  • XML: 41
  • Total: 865
  • BibTeX: 33
  • EndNote: 38
Views and downloads (calculated since 21 Apr 2023)
Cumulative views and downloads (calculated since 21 Apr 2023)

Viewed (geographical distribution)

Total article views: 849 (including HTML, PDF, and XML) Thereof 849 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 May 2024
Download
Short summary
Mesoscale eddies are ubiquitous in the ocean and account for 90 % of its kinetic energy, but their generation and dissipation struggle to observe with current remote sensing technology. Our submesoscale eddy dataset, formed by suppressing large-scale circulation signals and enhancing small-scale chlorophyll structures, has important implications for understanding marine environments and ecosystems, as well as improving climate model predictions.
Altmetrics