HIDRA-T – A Transformer-Based Sea Level Forecasting Method

Sea surface height forecasting is critical for timely prediction of coastal flooding and mitigation of is impact on coastal comminities. Traditional numerical ocean models are limited in terms of computational cost and accuracy, while deep learning models have shown promising results in this area. However, there is still a need for more accurate and efficient deep learning architectures for sea level and storm surge modeling. In this context, we propose a new deep-learning architecture HIDRA-T for sea level and storm tide modeling, which is based on transformers and outperforms both state-of-the-art deep-learning network designs HIDRA1 and HIDRA2 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. Compared to its predecessor HIDRA2, HIDRA-T employs novel transformer-based atmospheric and sea level encoders, as well as a novel feature fusion and regression block. HIDRA-T was trained on surface wind and pressure fields from ECMWF atmospheric ensemble and on Koper tide gauge observations. Compared to other models, a consistent superior performance over all other models is observed in the extreme tail of the sea level distribution.