Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 5
Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-2177-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2177-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 5
Development and technical paper
 | 
17 Mar 2026
Development and technical paper |  | 17 Mar 2026

HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data

Marko Rus, Matjaž Ličer, and Matej Kristan

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Short summary
This paper introduces HIDRA-D, a novel deep-learning model for dense, gridded sea level forecasting from sparse satellite altimetry and tide gauge data. By forecasting low-frequency spatial components, HIDRA-D offers a faster alternative to traditional numerical models. Evaluated on the satellite altimetry data in the Adriatic Sea, it outperforms the NEMO general circulation model, reducing the mean absolute error by 28.0 %. The model is robust but shows limitations in complex coastal areas.
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