Application of HIDRA2 Deep Learning Model for Sea Level Forecasting Along the Estonian Coast of the Baltic Sea

Abstract. Sea level predictions, typically derived from 3D hydrodynamic models, are computationally intensive and subject to uncertainties stemming from physical representation and inaccuracies in initial or boundary conditions. As a complementary alternative, data-driven machine learning models provide a computationally efficient solution with comparable accuracy. This study employs the deep learning model HIDRA2 to forecast hourly sea levels at five coastal stations along the Estonian coastline of the Baltic Sea, evaluating its performance across various forecast lead times. Compared to the regional NEMO_BAL and subregional NEMO_EST hydrodynamic models, HIDRA2 consistently delivers superior results, particularly across all sea level ranges and stations. While HIDRA2 struggles to capture high-frequency variability above (6 h)^-1, it effectively reproduces energy in lower-frequency bands below (18 h)^-1. Errors tend to average out over longer time windows encompassing multiple seiche periods, enabling HIDRA2 to surpass the overall performance of the NEMO models. These findings underscore HIDRA2’s potential as a robust, efficient, and reliable tool for operational sea level forecasting and coastal management in the Eastern Baltic Sea region.