Articles | Volume 14, issue 4
Geosci. Model Dev., 14, 2057–2074, 2021
https://doi.org/10.5194/gmd-14-2057-2021
Geosci. Model Dev., 14, 2057–2074, 2021
https://doi.org/10.5194/gmd-14-2057-2021

Model description paper 21 Apr 2021

Model description paper | 21 Apr 2021

HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic

Lojze Žust et al.

Data sets

NEMO, HIDRA and Tide Gauge Datasets for HIDRA Machine Learning Algorithm Verification L. Žust, M. Kristan, A. Fettich, and M. Licer https://doi.org/10.5281/zenodo.4106440

NEMO Configuration Namelist L. Žust, A. Fettich, M. Kristan, and M. Licer https://doi.org/10.5281/zenodo.4419333

Model code and software

lojzezust/HIDRA: HIDRA v1.0.1 L. Žust, A. Fettich, M. Kristan, and M. Licer https://doi.org/10.5281/zenodo.4457305

Download
Short summary
Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.