Articles | Volume 14, issue 1
Geosci. Model Dev., 14, 645–659, 2021
Geosci. Model Dev., 14, 645–659, 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 et al.

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Cited articles

Anderson, J. L.: A Local Least Squares Framework for Ensemble Filtering, Mon. Weather Rev., 131, 634–642,<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,, 2019. a
Bajo, M.: SHYFEM v. 7_5_65 with the data assimilation code version ens2.1,, 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,, 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,, 2019. a, b, c
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.