دورية أكاديمية
Deep Learning Improves Reconstruction of Ocean Vertical Velocity
| العنوان: | Deep Learning Improves Reconstruction of Ocean Vertical Velocity |
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| المؤلفون: | Ruichen Zhu, Yanqin Li, Zhaohui Chen, Tianshi Du, Yueqi Zhang, Zhuoran Li, Zhiyou Jing, Haiyuan Yang, Zhao Jing, Lixin Wu |
| المصدر: | Geophysical Research Letters, Vol 50, Iss 19, Pp n/a-n/a (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | deep learning, ocean vertical velocity, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Abstract Ocean vertical velocity (w) plays a key role in regulating the exchanges of mass, heat and nutrients between the surface and deep ocean. However, direct observation remains difficult due to its small magnitude and large spatiotemporal variability. Therefore, w fields are generally diagnosed using dynamic‐based methods. In this study, we developed a deep neural network (DNN) to reconstruct three‐dimensional fields of ocean vertical velocity based on sea surface height (SSH) fields. Compared to dynamic‐based methods, the DNN shows improved performance in the w reconstruction within upper 500 m in terms of higher correlation and less error. Remarkably, the DNN requires only a ∼45 × 45 km size SSH image as input to estimate w at the center. This suggests that the DNN has great potential for w reconstruction in the future combined with high‐resolution observations such as the Surface Water and Ocean Topography mission. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1944-8007 0094-8276 |
| Relation: | https://doaj.org/toc/0094-8276; https://doaj.org/toc/1944-8007 |
| DOI: | 10.1029/2023GL104889 |
| URL الوصول: | https://doaj.org/article/d19ac9e5798f46538949badb1c8fcfd3 |
| رقم الأكسشن: | edsdoj.19ac9e5798f46538949badb1c8fcfd3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19448007 00948276 |
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| DOI: | 10.1029/2023GL104889 |