Integrating Satellite Altimetry and Deep Learning for Enhanced Sea Level Forecasting

Sea level forecasting is crucial for safeguarding coastal regions, enhancing marine infrastructure, ensuring navigational safety, and mitigating natural hazards. Traditional methods, which often rely on single data sources like tide gauges or merged altimeters, may struggle to provide accurate predictions in highly dynamic regions due to their limited spatial and temporal resolution. Multi-Sensor Data Fusion has the potential to address these limitations by integrating data from multiple sensors, thereby improving the consistency, quality, and accuracy of predictions. While this approach has been successfully applied to other oceanographic parameters such as sea surface temperature (SST), sea ice, and salinity, sea level prediction remains challenging due to the spatial constraints of tide gauges and the relatively low temporal frequency of satellite observations. New satellite altimetry missions, such as the Surface Water and Ocean Topography (SWOT) mission, can deliver high-resolution data. When combined with deep learning (DL) techniques, this has the potential to improve the accuracy of sea level forecasting.

This research presents a novel DL approach for sea surface height (SSH) forecasting based on SWOT satellite data. By incorporating data from various sources (e.g. tide gauges, hydrodynamic models, and meteorological variables, such as wind speed and atmospheric pressure), the model aims to provide accurate, instantaneous SSH maps referenced to the geoid surface (i.e. Dynamic Topography (DT)). The DL architecture also encompasses attention mechanisms to prioritize spatially and temporally critical information, potentially overcoming the inefficiencies seen in traditional data assimilation methods.

The goal is to generate DT maps for real-time, adaptable predictions by integrating spatio-temporal data sources. Unlike previous methods, this approach incorporates instantaneous DT directly into the modeling process rather than solely for validation. The approach will be evaluated in the Baltic Sea to assess its precision. This model has the potential to set a new benchmark for sea level forecasting, offering valuable contributions to environmental monitoring, climate research, operational oceanography, and decision-making by providing high-resolution and interpretable predictions.