Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-645-2021
https://doi.org/10.5194/gmd-14-645-2021
Development and technical paper
 | 
01 Feb 2021
Development and technical paper |  | 01 Feb 2021

Model-driven optimization of coastal sea observatories through data assimilation in a finite element hydrodynamic model (SHYFEM v. 7_5_65)

Christian Ferrarin, Marco Bajo, and Georg Umgiesser

Related authors

Sea Level Rise in Europe: Impacts and consequences
Roderik van de Wal, Angélique Melet, Debora Bellafiore, Paula Camus, Christian Ferrarin, Gualbert Oude Essink, Ivan D. Haigh, Piero Lionello, Arjen Luijendijk, Alexandra Toimil, Joanna Staneva, and Michalis Vousdoukas
State Planet, 3-slre1, 5, https://doi.org/10.5194/sp-3-slre1-5-2024,https://doi.org/10.5194/sp-3-slre1-5-2024, 2024
Short summary
Dynamics, predictability, impacts, and climate change considerations of the catastrophic Mediterranean Storm Daniel (2023)
Emmanouil Flaounas, Stavros Dafis, Silvio Davolio, Davide Faranda, Christian Ferrarin, Katharina Hartmuth, Assaf Hochman, Aristeidis Koutroulis, Samira Khodayar, Mario Marcello Miglietta, Florian Pantillon, Platon Patlakas, Michael Sprenger, and Iris Thurnherr
EGUsphere, https://doi.org/10.5194/egusphere-2024-2809,https://doi.org/10.5194/egusphere-2024-2809, 2024
Short summary
A flexible z-layers approach for the accurate representation of free surface flows in a coastal ocean model (SHYFEM v. 7_5_71)
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023,https://doi.org/10.5194/gmd-16-6899-2023, 2023
Short summary
Assessing the coastal hazard of Medicane Ianos through ensemble modelling
Christian Ferrarin, Florian Pantillon, Silvio Davolio, Marco Bajo, Mario Marcello Miglietta, Elenio Avolio, Diego S. Carrió, Ioannis Pytharoulis, Claudio Sanchez, Platon Patlakas, Juan Jesús González-Alemán, and Emmanouil Flaounas
Nat. Hazards Earth Syst. Sci., 23, 2273–2287, https://doi.org/10.5194/nhess-23-2273-2023,https://doi.org/10.5194/nhess-23-2273-2023, 2023
Short summary
Modelling the barotropic sea level in the Mediterranean Sea using data assimilation
Marco Bajo, Christian Ferrarin, Georg Umgiesser, Andrea Bonometto, and Elisa Coraci
Ocean Sci., 19, 559–579, https://doi.org/10.5194/os-19-559-2023,https://doi.org/10.5194/os-19-559-2023, 2023
Short summary

Related subject area

Oceanography
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025,https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
A wave-resolving two-dimensional vertical Lagrangian approach to model microplastic transport in nearshore waters based on TrackMPD 3.0
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025,https://doi.org/10.5194/gmd-18-319-2025, 2025
Short summary
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025,https://doi.org/10.5194/gmd-18-211-2025, 2025
Short summary
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024,https://doi.org/10.5194/gmd-17-8697-2024, 2024
Short summary
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024,https://doi.org/10.5194/gmd-17-8553-2024, 2024
Short summary

Cited articles

Anderson, J. L.: A Local Least Squares Framework for Ensemble Filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003. a
Androsov, A., Fofonova, V., Kuznetsov, I., Danilov, S., Rakowsky, N., Harig, S., Brix, H., and Wiltshire, K. H.: FESOM-C v.2: coastal dynamics on hybrid unstructured meshes, Geosci. Model Dev., 12, 1009-1028, https://doi.org/10.5194/gmd-12-1009-2019, 2019. a
Bajo, M.: SHYFEM v. 7_5_65 with the data assimilation code version ens2.1, https://doi.org/10.5281/zenodo.3757843, 2020. a, b
Bajo, M., De Biasio, F., Umgiesser, G., Vignudelli, S., and Zecchetto, S.: Impact of using scatterometer and altimeter data on storm surge forecasting, Ocean Model., 113, 85–94, https://doi.org/10.1016/j.ocemod.2017.03.014, 2017. a
Bajo, M., Medugorac, I., Umgiesser, G., and Orlić, M.: Storm surge and seiche modelling in the Adriatic Sea and the impact of data assimilation, Q. J. Roy. Meteor. Soc., 145, 2070–2084, https://doi.org/10.1002/qj.3544, 2019. a, b, c
Download
Short summary
The problem of the optimization of ocean monitoring networks is tackled through the implementation of data assimilation techniques in a numerical model. The methodology has been applied to the tide gauge network in the Lagoon of Venice (Italy). The data assimilation methods allow identifying the minimum number of stations and their distribution that correctly represent the lagoon's dynamics. The methodology is easily exportable to other environments and can be extended to other variables.
Share