MedFormer: a data-driven model for forecasting the Mediterranean Sea
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Epicoco, Italo Donno, Davide Accarino, Gabriele Norberti, Simone Grandi, Alessandro Giurato, Michele McAdam, Ronan Elia, Donatello Clementi, Emanuela Nassisi, Paola Scoccimarro, Enrico Coppini, Giovanni Gualdi, Silvio Aloisio, Giovanni Masina, Simona Boccaletti, Giulio Navarra, Antonio |
| author_facet | Epicoco, Italo Donno, Davide Accarino, Gabriele Norberti, Simone Grandi, Alessandro Giurato, Michele McAdam, Ronan Elia, Donatello Clementi, Emanuela Nassisi, Paola Scoccimarro, Enrico Coppini, Giovanni Gualdi, Silvio Aloisio, Giovanni Masina, Simona Boccaletti, Giulio Navarra, Antonio |
| contents | Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven models to outperform traditional numerical approaches, particularly in atmospheric weather forecasting. However, extending these methods to ocean systems remains challenging due to their inherently slower dynamics and complex boundary conditions. In this work, we present MedFormer, a fully data-driven deep learning model specifically designed for medium-range ocean forecasting in the Mediterranean Sea. MedFormer is based on a U-Net architecture augmented with 3D attention mechanisms and operates at a high horizontal resolution of 1/24°. The model is trained on 20 years of daily ocean reanalysis data and fine-tuned with high-resolution operational analyses. It generates 9-day forecasts using an autoregressive strategy. The model leverages both historical ocean states and atmospheric forcings, making it well-suited for operational use. We benchmark MedFormer against the state-of-the-art Mediterranean Forecasting System (MedFS), developed at Euro-Mediterranean Center on Climate Change (CMCC), using both analysis data and independent observations. The forecast skills, evaluated with the Root Mean Squared Difference and the Anomaly Correlation Coefficient, indicate that MedFormer consistently outperforms MedFS across key 3D ocean variables. These findings underscore the potential of data-driven approaches like MedFormer to complement, or even surpass, traditional numerical ocean forecasting systems in both accuracy and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedFormer: a data-driven model for forecasting the Mediterranean Sea Epicoco, Italo Donno, Davide Accarino, Gabriele Norberti, Simone Grandi, Alessandro Giurato, Michele McAdam, Ronan Elia, Donatello Clementi, Emanuela Nassisi, Paola Scoccimarro, Enrico Coppini, Giovanni Gualdi, Silvio Aloisio, Giovanni Masina, Simona Boccaletti, Giulio Navarra, Antonio Atmospheric and Oceanic Physics Machine Learning I.2.1; J.2 Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven models to outperform traditional numerical approaches, particularly in atmospheric weather forecasting. However, extending these methods to ocean systems remains challenging due to their inherently slower dynamics and complex boundary conditions. In this work, we present MedFormer, a fully data-driven deep learning model specifically designed for medium-range ocean forecasting in the Mediterranean Sea. MedFormer is based on a U-Net architecture augmented with 3D attention mechanisms and operates at a high horizontal resolution of 1/24°. The model is trained on 20 years of daily ocean reanalysis data and fine-tuned with high-resolution operational analyses. It generates 9-day forecasts using an autoregressive strategy. The model leverages both historical ocean states and atmospheric forcings, making it well-suited for operational use. We benchmark MedFormer against the state-of-the-art Mediterranean Forecasting System (MedFS), developed at Euro-Mediterranean Center on Climate Change (CMCC), using both analysis data and independent observations. The forecast skills, evaluated with the Root Mean Squared Difference and the Anomaly Correlation Coefficient, indicate that MedFormer consistently outperforms MedFS across key 3D ocean variables. These findings underscore the potential of data-driven approaches like MedFormer to complement, or even surpass, traditional numerical ocean forecasting systems in both accuracy and computational efficiency. |
| title | MedFormer: a data-driven model for forecasting the Mediterranean Sea |
| topic | Atmospheric and Oceanic Physics Machine Learning I.2.1; J.2 |
| url | https://arxiv.org/abs/2509.00015 |