Deep-learning transformer-based sea level modeling ensemble for the Adriatic basin

Storm surges and coastal floods are persistent threats to civil and economic safety in the Northern Adriatic. Meteorologically induced sea level signal is, however, often difficult to forecast deterministically due to the resonant character of the Adriatic basin. A standard solution is therefore resorting to ensembles of numerical ocean models, which are numerically expensive. In recent years, deep-learning-based methods have shown significant potential for numerically cheap alternatives. This is the venue followed in our work. We propose a new deep-learning transformer-based architecture HIDRA-T, a continuation of our recent model HIDRA2 (Rus et al., GMD 2023), which outperformed both state-of-the-art deep-learning network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine and a SCHISM ocean modeling system). HIDRA-T is our latest attempt at sea level forecasting, employing novel transformer-based atmospheric and sea level encoders. Transformers are designed for sequential data, and in HIDRA-T we use self-attention blocks to extract features from the atmospheric data firstly by tokenizing over spatial dimension, then over temporal dimension. HIDRA-T was trained on surface wind and pressure fields from the ECMWF atmospheric ensemble and on Koper tide gauge observations. On an independent and challenging test set, HIDRA-T outperforms all other models, reducing previous best mean absolute forecast error in storm events of HIDRA2 by 2.6 %.