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.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lojze Žust on behalf of the Authors (05 Jan 2021)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (20 Jan 2021) by Xiaomeng Huang
AR by Lojze Žust on behalf of the Authors (26 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (09 Feb 2021) by Xiaomeng Huang
AR by Lojze Žust on behalf of the Authors (17 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 Mar 2021) by Xiaomeng Huang
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.