The assessment of temperature and salinity sampling strategies in the Mediterranean Sea: idealized and real cases

Raicich, Fabio

doi:10.5194/os-2-97-2006

Cited by 13 publications

(9 citation statements)

References 32 publications

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“…This method has been used to evaluate sampling strategies through assessing the impact of “hypothetical” observations on improving model abilities for weather forecasting with data assimilation. This method has been adopted to evaluate the design of a mooring array in the tropic Atlantic Ocean [ Hackert et al , 1998]; test the feasibility of a mooring system for the meridional overturning circulation in the North Atlantic Ocean [ Hirschi et al , 2003]; infer sampling strategies of the Argo array in the Indian Ocean [ Schiller et al , 2004]; sample the water properties with optimal interpolation in the Mediterranean Sea [ Raicich , 2006]; and examine the design of a proposed array of instrumented moorings in the Indian Ocean [ Ballabrera‐Poy et al , 2007]. Recently, OSSEs have also been used in coastal oceans to optimize fixed observational assets [ Frolov et al , 2008], constrain sensor placement from noisy ocean measurements [ Yang et al , 2010], and assess a monitoring network in a coastal region with multiscale processes [ Xue et al , 2011].…”

Section: Introductionmentioning

confidence: 99%

Observing system simulation experiments of dissolved oxygen monitoring in Massachusetts Bay

Xue¹,

Chen²,

Beardsley³

2012

J. Geophys. Res.

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[1] Observing system simulation experiments (OSSEs) were performed in Massachusetts Bay for the design of optimal monitoring sites for dissolved oxygen (DO) measurements. Experiments were carried out using the Ensemble Kalman Filter (EnKF) for data assimilation with focus on initial and boundary perturbations. Running a well-validated water quality model with a perturbed initial field of DO but "true" boundary forcing conditions, the model is capable of restoring DO back to the true state without data assimilation over a recovery time scale of about a month. Since DO in Massachusetts Bay has a bay-wide correlation scale, placing a monitoring site of DO near the northern boundary or at a location that has maximum correlation to the entire domain can shorten the restoring time to a week. Running the model with perturbed boundary forcing without data assimilation, the results show that the errors propagate into Massachusetts Bay following the inflow from the northern boundary and spread southward to Cape Cod Bay over a time scale of about a month. Using a DO monitoring site located near the northern entrance, the data assimilation can efficiently control the error propagation and prevent the model field from deviating from the true state. The model shows that the inflow from the northern entrance, which is connected to the upstream Western Maine Coastal Current, plays an important role in controlling the DO variation in Massachusetts Bay, and the residence time of the bay controlled by this flow is about one month. Understanding the upstream boundary-control nature of this system is critical for optimal design of sampling strategies of water quality variables in this region.

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Section: Introductionmentioning

confidence: 99%

Observing system simulation experiments of dissolved oxygen monitoring in Massachusetts Bay

Xue¹,

Chen²,

Beardsley³

2012

J. Geophys. Res.

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“…However, the methodology could be applied also to design new monitoring networks. As described by Raicich (2006) and Xue et al (2011), in an observing system simulation experiment, synthetic observations are generated by a model run in some locations and then they are assimilated as real observations. The procedure is similar to a twin experiment, a method used to assess the quality of a data assimilation system.…”

Section: Discussionmentioning

confidence: 99%

“…Validated ocean circulation model and DA can also assist the network design of a new observing system or optimizing existing observatory (Fujii et al, 2019). In the case of new monitoring networks, Observing System Simulation Experiments (OSSEs) are performed assimilating synthetic observation data (generated from a free-running model simulation that is intended to represent a virtual "true" ocean) into other data-assimilative simulation runs in which different initial/forcing conditions are used (Raicich, 2006;Xue et al, 2011). The evaluation of the impact of the assimilated data in the OSSE simulations allows designing an optimal observing system.…”

Section: Introductionmentioning

confidence: 99%

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

Ferrarin¹,

Bajo²,

Umgiesser³

2020

Preprint

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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.

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“…Localization is often used in ensemble data assimilation and can be done following different methods (Houtekamer and Mitchell, 2001;Hamill et al, 2001;Anderson, 2003). In the present case we used a local analysis method, which performs in a similar way to the covariance localization method (Sakov and Bertino, 2011). This method reduces the influence of the observations too far from the location of the model variable which is going to be modified.…”

Section: Ensemble Square Root Filtermentioning

confidence: 99%

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

2021

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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 results in an improvement of the performance of the SHYFEM model, which 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 each of the two data assimilation methods. 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 a 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.

show abstract

The assessment of temperature and salinity sampling strategies in the Mediterranean Sea: idealized and real cases

Cited by 13 publications

References 32 publications

Observing system simulation experiments of dissolved oxygen monitoring in Massachusetts Bay

Observing system simulation experiments of dissolved oxygen monitoring in Massachusetts Bay

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

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

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