Preprints
https://doi.org/10.5194/gmd-2020-61
https://doi.org/10.5194/gmd-2020-61

Submitted as: development and technical paper 18 May 2020

Submitted as: development and technical paper | 18 May 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Christian Ferrarin1, Marco Bajo1, and Georg Umgiesser1,2 Christian Ferrarin et al.
  • 1CNR - National Research Council of Italy, ISMAR - Marine Sciences Institute, Venice, Italy
  • 2Marine Research Institute, Klaipeda University, Klaipeda, Lithuania

Abstract. Monitoring networks aims at capturing the spatial and temporal variability of one or several environmental variables in a specific environment. The optimal placement of sensors in an ocean or coastal observatory should maximize the amount of collected information and minimize the development and operational costs for the whole monitoring network. In this study, the problem of the design and optimization of ocean monitoring networks is tackled throughout the implementation of data assimilation techniques in the Shallow water Hydrodynamic Finite Element Model (SHYFEM). Two data assimilation methods – Nudging and Ensemble Square Root Filter – have been applied and tested in the Lagoon of Venice (Italy), where an extensive water level monitoring network exists. A total of 29 tide gauge stations were available and the assimilation of the observations result in an improvement of the performance of the SHYFEM model that went from an initial root mean square error (RMSE) on the water level of 5.8 cm to a final value of about 2.1 and 3.2 cm for the two data assimilation methods, respectively. In the monitoring network optimization procedure, by excluding just one tide gauge at a time, and always the station that contributes less to the improvement of the RMSE, a minimum number of tide gauges can be found that still allow for a successful description of the water level variability. Both data assimilation methods allow identifying the number of stations and their distribution that correctly represent the state variable in the investigated system. However, the more advanced Ensemble Square Root Filter has the benefit of keeping a physically and mass conservative solution of the governing equations, which results in a better reproduction of the hydrodynamics over the whole system. In the case of the Lagoon of Venice, we found that, with the help of a process-based and observation-driven numerical model, two-thirds of the monitoring network can be dismissed. In this way, if some of the stations must be decommissioned due to a lack of funding, an a-priori choice can be made, and the importance of the single monitoring site can be evaluated. The developed procedure may also be applied to the continuous monitoring of other ocean variables, like sea temperature and salinity.

Christian Ferrarin et al.

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

Christian Ferrarin et al.

Data sets

SHYFEM set-up for model driven optimization of the tide gauge monitoring network in the Lagoon of Venice C. Ferrarin, M. Bajo, and G. Umgiesser https://doi.org/10.5281/zenodo.3770173

Model code and software

SHYFEM v. 7_5_65 G. Umgiesser https://doi.org/10.5281/zenodo.3757785

SHYFEM v. 7_5_65 with the data assimilation code version ens2.1 M. Bajo https://doi.org/10.5281/zenodo.3757843

Christian Ferrarin et al.

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Short summary
The problem of the optimization of ocean monitoring networks is tackled throughout the implementation of data assimilation techniques in a numerical model. The methodology has been applied and tested 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. With the help of the numerical model, two-thirds of the monitoring network can be dismissed.