Articles | Volume 16, issue 1
Model description paper
10 Jan 2023
Model description paper |  | 10 Jan 2023

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-618', Juan Antonio Añel, 21 Sep 2022
    • CC1: 'Reply on CEC1', Matjaz Licer, 22 Sep 2022
    • CC2: 'Reply on Editor comment. License and Input Training/Testing datasets.', Matjaz Licer, 30 Sep 2022
  • RC1: 'Comment on egusphere-2022-618', Anonymous Referee #1, 25 Sep 2022
  • RC2: 'Comment on egusphere-2022-618', Anonymous Referee #2, 10 Oct 2022
  • RC3: 'Comment on egusphere-2022-618', Anonymous Referee #3, 31 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Marko Rus on behalf of the Authors (14 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (15 Nov 2022) by Rohitash Chandra
RR by Anonymous Referee #1 (15 Nov 2022)
RR by Anonymous Referee #2 (22 Nov 2022)
ED: Publish as is (03 Dec 2022) by Rohitash Chandra
AR by Marko Rus on behalf of the Authors (06 Dec 2022)  Manuscript 
Short summary
We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.