Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea
<p>Sea level predictions, typically derived from 3D hydrodynamic models, are computationally intensive and subject to uncertainties stemming from physical representation and inaccuracies in initial or boundary conditions. As a complementary alternative, data-driven machine learning models prov...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | Ocean Science |
| Online Access: | https://os.copernicus.org/articles/21/1315/2025/os-21-1315-2025.pdf |
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| Summary: | <p>Sea level predictions, typically derived from 3D hydrodynamic models, are computationally intensive and subject to uncertainties stemming from physical representation and inaccuracies in initial or boundary conditions. As a complementary alternative, data-driven machine learning models provide a computationally efficient solution with comparable accuracy. This study employs the deep-learning model HIDRA2 to forecast hourly sea levels at five coastal stations along the Estonian coastline of the Baltic Sea, evaluating its performance across various forecast lead times. Compared to the regional NEMO<span class="inline-formula"><sub>BAL</sub></span> and subregional NEMO<span class="inline-formula"><sub>EST</sub></span> hydrodynamic models, HIDRA2 frequently outperforms both, particularly in terms of overall forecast skill. While HIDRA2 shows limitations in resolving high-frequency sea level variability above (6 <span class="inline-formula">h</span>)<span class="inline-formula"><sup>−1</sup></span>, it effectively reproduces energy in lower-frequency bands below (18 <span class="inline-formula">h</span>)<span class="inline-formula"><sup>−1</sup></span>. Errors tend to average out over longer time windows encompassing multiple seiche periods, enabling HIDRA2 to surpass the overall performance of the NEMO models. These findings underscore HIDRA2's potential as a robust, efficient, and reliable tool for operational sea level forecasting and coastal management in the eastern Baltic Sea region.</p> |
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| ISSN: | 1812-0784 1812-0792 |