Reconstructing and Forecasting Marine Dynamic Variable Fields across Space and Time Globally and Gaplessly ...
Spatiotemporal projections in marine science are essential for understanding ocean systems and their impact on Earth's climate. However, existing AI-based and statistics-based inversion methods face challenges in leveraging ocean data, generating continuous outputs, and incorporating physical c...
| Main Authors: | , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
arXiv
2024
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.48550/arxiv.2408.01509 https://arxiv.org/abs/2408.01509 |
| Summary: | Spatiotemporal projections in marine science are essential for understanding ocean systems and their impact on Earth's climate. However, existing AI-based and statistics-based inversion methods face challenges in leveraging ocean data, generating continuous outputs, and incorporating physical constraints. We propose the Marine Dynamic Reconstruction and Forecast Neural Networks (MDRF-Net), which integrates marine physical mechanisms and observed data to reconstruct and forecast continuous ocean temperature-salinity and dynamic fields. MDRF-Net leverages statistical theories and techniques, incorporating parallel neural network sharing initial layer, two-step training strategy, and ensemble methodology, facilitating in exploring challenging marine areas like the Arctic zone. We have theoretically justified the efficacy of our ensemble method and the rationality of it by providing an upper bound on its generalization error.The effectiveness of MDRF-Net's is validated through a comprehensive simulation study, ... : 35 pages, 6 figures ... |
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