Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean
Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the BLT using satellite observations in the tropical Ind...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Environmental Research Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7620/acf9e1 |
| _version_ | 1797674451384401920 |
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| author | Jifeng Qi Tangdong Qu Baoshu Yin |
| author_facet | Jifeng Qi Tangdong Qu Baoshu Yin |
| author_sort | Jifeng Qi |
| collection | DOAJ |
| description | Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the BLT using satellite observations in the tropical Indian Ocean. The results show that the meta-learning-based ensemble model outperforms the three individual models in terms of spatial distribution and accuracy, with significantly reduced root mean square errors in the Southeast Arabian Sea, Bay of Bengal, and eastern equatorial Indian Ocean. Furthermore, we found that sea surface salinity plays the most significant role in the estimation of BLT, highlighting the dominant influence of salinity stratification. These preliminary results provide an insight into the feasibility of predicting the BLT using satellite observations and have implications for studying the upper ocean dynamics using machine learning techniques. |
| first_indexed | 2024-03-11T22:00:13Z |
| format | Article |
| id | doaj.art-f65b4aaae3f5421491dd6383957b0dac |
| institution | Directory Open Access Journal |
| issn | 2515-7620 |
| language | English |
| last_indexed | 2024-03-11T22:00:13Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj.art-f65b4aaae3f5421491dd6383957b0dac2023-09-25T12:04:30ZengIOP PublishingEnvironmental Research Communications2515-76202023-01-015909100510.1088/2515-7620/acf9e1Meta-learning-based estimation of the barrier layer thickness in the tropical Indian OceanJifeng Qi0https://orcid.org/0000-0002-9156-1190Tangdong Qu1Baoshu Yin2CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences , Qingdao, People’s Republic of China; University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaJoint Institute for Regional Earth System Science and Engineering, University of California , Los Angeles, CA, United States of AmericaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences , Qingdao, People’s Republic of China; University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaAccurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the BLT using satellite observations in the tropical Indian Ocean. The results show that the meta-learning-based ensemble model outperforms the three individual models in terms of spatial distribution and accuracy, with significantly reduced root mean square errors in the Southeast Arabian Sea, Bay of Bengal, and eastern equatorial Indian Ocean. Furthermore, we found that sea surface salinity plays the most significant role in the estimation of BLT, highlighting the dominant influence of salinity stratification. These preliminary results provide an insight into the feasibility of predicting the BLT using satellite observations and have implications for studying the upper ocean dynamics using machine learning techniques.https://doi.org/10.1088/2515-7620/acf9e1Ocean barrier layer thicknessIndian Oceanmeta-learningmachine learning |
| spellingShingle | Jifeng Qi Tangdong Qu Baoshu Yin Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean Environmental Research Communications Ocean barrier layer thickness Indian Ocean meta-learning machine learning |
| title | Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean |
| title_full | Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean |
| title_fullStr | Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean |
| title_full_unstemmed | Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean |
| title_short | Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean |
| title_sort | meta learning based estimation of the barrier layer thickness in the tropical indian ocean |
| topic | Ocean barrier layer thickness Indian Ocean meta-learning machine learning |
| url | https://doi.org/10.1088/2515-7620/acf9e1 |
| work_keys_str_mv | AT jifengqi metalearningbasedestimationofthebarrierlayerthicknessinthetropicalindianocean AT tangdongqu metalearningbasedestimationofthebarrierlayerthicknessinthetropicalindianocean AT baoshuyin metalearningbasedestimationofthebarrierlayerthicknessinthetropicalindianocean |