arXiv subjects

Emanuela Clementi

Publications and source records attributed to Emanuela Clementi.

4 recordsLinked to original sources

arXiv2025

MedFormer: a data-driven model for forecasting the Mediterranean Sea

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{\deg}. 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.

physics.ao-ph↗

arXiv2025

Accurate Mediterranean Sea forecasting via graph-based deep learning

Accurate ocean forecasting systems are essential for understanding marine dynamics, which play a crucial role in sectors such as shipping, aquaculture, environmental monitoring, and coastal risk management. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution regional ocean forecasting. SeaCast employs a graph-based framework to effectively handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high horizontal resolution using the operational numerical forecasting system of the Mediterranean Sea, along with both numerical and data-driven atmospheric forcings. Results demonstrate that SeaCast consistently outperforms the operational model in forecast skill, marking a significant advancement in regional ocean prediction.

physics.ao-ph↗

arXiv2024

Internal tides in the Mediterranean Sea

The generation and propagation sites of internal tides in the Mediterranean Sea are mapped through a comprehensive high-resolution numerical study. Two ocean general circulation models were used for this: NEMO v3.6, and ICON-O, both hydrostatic ocean models based on primitive equations with Boussinesq approximation, where NEMO is a regional Mediterranean Sea model with an Atlantic box, and ICON a global model. Internal tides are widespread in the Mediterranean Sea. The primary generation sites: the Gibraltar Strait, Sicily Strait/Malta Bank, and Hellenic Arc, are mapped through analysis of the tidal barotropic to baroclinic energy conversion. Semidiurnal internal tides can propagate for hundreds of kilometres from these generation sites into the Algerian Sea, Tyrrhenian Sea, and Ionian Sea respectively. Diurnal internal tides remain trapped along the bathymetry, and are generated in the central Mediterranean Sea and southeastern coasts of the basin. The total energy used for internal tide generation in the Mediterranean Sea is 2.89 GW in NEMO and 1.36 GW in ICON. Wavelengths of the first baroclinic modes of the M2 tide are calculated in various regions of the Mediterranean Sea where internal tides are propagating, comparing model outputs to a theory-based calculation. The models are also intercompared to investigate the differences between them in their representation of internal tides.

physics.ao-ph↗

arXiv2024

Regional Ocean Forecasting with Hierarchical Graph Neural Networks

Accurate ocean forecasting systems are vital for understanding marine dynamics, which play a crucial role in environmental management and climate adaptation strategies. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to effectively handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high spatial resolution using the operational numerical model of the Mediterranean Sea provided by the Copernicus Marine Service, along with both numerical and data-driven atmospheric forcings.

physics.ao-ph↗