A submesoscale eddy identification dataset in the northwest Pacific Ocean derived from GOCI I chlorophyll <i>a</i> data based on deep learning
<p>This paper presents a dataset on the identification of submesoscale eddies, derived from high-resolution chlorophyll <span class="inline-formula"><i>a</i></span> data captured by GOCI I in the northwest Pacific Ocean. Our methodology involves a combination...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2024-12-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/5737/2024/essd-16-5737-2024.pdf |
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| Summary: | <p>This paper presents a dataset on the identification of submesoscale eddies, derived from high-resolution chlorophyll <span class="inline-formula"><i>a</i></span> data captured by GOCI I in the northwest Pacific Ocean. Our methodology involves a combination of digital image processing, filtering, and object detection techniques, along with a specific chlorophyll <span class="inline-formula"><i>a</i></span> image enhancement procedure to extract essential information about submesoscale eddies. This information includes 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. The dataset spans eight time intervals, ranging from 00:00 to 08:00 (UTC) daily, covering the period from 1 April 2011 to 31 March 2021. A total of 19 136 anticyclonic eddies and 93 897 cyclonic eddies were identified, with a minimum confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 to 44 km). This unprecedented hourly resolution dataset on submesoscale eddies offers valuable insights into their distribution, morphology, and energy dissipation. It significantly contributes to our understanding of marine environments, ecosystems, and the improvement of climate model predictions. The dataset is available at <a href="https://doi.org/10.5281/zenodo.13989785">https://doi.org/10.5281/zenodo.13989785</a> (Wang and Yang, 2023).</p> |
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| ISSN: | 1866-3508 1866-3516 |