Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-271-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-271-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 1,805 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,334 424 47 1,805 49 28 28
  • HTML: 1,334
  • PDF: 424
  • XML: 47
  • Total: 1,805
  • Supplement: 49
  • BibTeX: 28
  • EndNote: 28
Views and downloads (calculated since 29 Aug 2022)
Cumulative views and downloads (calculated since 29 Aug 2022)

Viewed (geographical distribution)

Total article views: 1,805 (including HTML, PDF, and XML) Thereof 1,717 with geography defined and 88 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Apr 2024
Download
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.
Read more