Assimilating XBT Data into HYCOM

Thacker, W. C.; Esenkov, Oleg E.

doi:10.1175/1520-0426(2002)019<0709:axdih>2.0.co;2

Cited by 23 publications

(15 citation statements)

References 30 publications

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“…Also, the reorganization of the isopycnal layers by the assimilation of p obs has provided a large reduction of the diagnosed SSH in the model, reflecting the new thermohaline state achieved by Argo data assimilation. Indeed, the correction of layer thickness played a role in correcting the model thermohaline structure and stratification, as stated in previous data assimilation works with HYCOM (Thacker and Esenkov, 2002;Chin et al, 2002;Thacker et al, 2004;Xie and Zhu, 2010). The vertical localization of T and S did not produce any significant impact in the assimilation experiments, which is consistent with the results found in XZ.…”

Section: Conclusion and Discussionsupporting

confidence: 89%

“…The vertical localization of only T and S had almost no positive impacts on the RMSD, and contributed to slightly degrading S near the surface when the runs were compared to PIRATA data. This is mainly because the most important HYCOM variable to determine the thermohaline structure is the model layer thickness (Thacker and Esenkov, 2002;Thacker et al, 2004;Xie and Zhu, 2010). For instance, according to HYCOM formulation, if the analysis increments of T and S decrease the density of a specific layer in comparison to its target density, the model attempts to move the lower interface downward, so that the flux of denser water across this interface increases the layer density (Bleck, 2002).…”

Section: Comparison With Argo Profilers and Pirata Mooringsmentioning

confidence: 99%

“…Taking into account these characteristics, Thacker and Esenkov (2002;hereafter TE) proposed a method to assimilate expendable bathythermographic (XBT) data into HYCOM with a three-dimensional variational scheme . In this work, the XBT profiles are converted into observed layer thicknesses that respect the target densities of each model layer, and the temperature and salinity of the XBTs are projected to each observed layer thickness.…”

Section: Introductionmentioning

confidence: 99%

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Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

et al. 2015

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Abstract. An ocean data assimilation system to assimilate Argo temperature (T ) and salinity (S) profiles into the HYbrid Coordinate Ocean Model (HYCOM) was constructed, implemented and evaluated for the first time in the Atlantic Ocean (78 • S to 50 • N and 98 • W to 20 • E). The system is based on the ensemble optimal interpolation (EnOI) algorithm proposed by Xie and Zhu (2010), especially made to deal with the hybrid nature of the HYCOM vertical coordinate system with multiple steps. The Argo T -S profiles were projected to the model vertical space to create pseudoobserved layer thicknesses ( p obs ), which correspond to the model target densities. The first step was to assimilate p obs considering the sub-state vector composed by the model layer thickness ( p) and the baroclinic velocity components. After that, T and S were assimilated separately. Finally, T was diagnosed below the mixed layer to preserve the density of the model isopycnal layers. Five experiments were performed from 1 January 2010 to 31 December 2012: a control run without assimilation, and four assimilation runs considering the different vertical localizations of T , S and p. The assimilation experiments were able to significantly improve the thermohaline structure produced by the control run. They reduced the root mean square deviation (RMSD) of T and S calculated with respect to Argo independent data in 34 and 44 %, respectively, in comparison to the control run. In some regions, such as the western North Atlantic, substantial corrections in the 20 • C isotherm depth and the upper ocean heat content towards climatological states were achieved.The runs with a vertical localization of p showed positive impacts in the correction of the thermohaline structure and reduced the RMSD of T (S) from 0.993 • C (0.149 psu) to 0.905 • C (0.138 psu) for the whole domain with respect to the other assimilation runs.

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Section: Conclusion and Discussionsupporting

confidence: 89%

Section: Comparison With Argo Profilers and Pirata Mooringsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

et al. 2015

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“…Thus, using simple assimilation schemes, it is not known, a priori, how to update the water-mass properties in a consistent manner, see, e.g., Troccoli et al (2002) and Thacker and Esenkov (2002).…”

Section: Appendix C: Assimilation Of In Situ Measurementsmentioning

confidence: 99%

The Ensemble Kalman Filter: theoretical formulation and practical implementation

Evensen¹

2003

Ocean Dynamics

3,664

2,898

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The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

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“…A common remedy for the data density discrepancy issue is to use a ''superobservation'' (or ''superobs''), a surrogate datum that replaces several partially redundant actual data within a computational bin, thereby avoiding the data density issue (e.g., Thacker and Esenkov 2002). However, a typical superobs is essentially a bin average that tends to lose the desirable information on intragrid variability (Kaplan et al 2003).…”

Section: Introductionmentioning

confidence: 99%

On “Gridless” Interpolation and Subgrid Data Density

Chin

Vázquez-Cuervo

Armstrong

2014

Journal of Atmospheric and Oceanic Technology

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Nearest-neighbor gridding, binning, and bin-averaging procedures are performed routinely to map the irregularly sampled data onto a grid for data analysis and assimilation. Because these procedures are actually an interpolation procedure based on a piecewise constant function as the interpolation kernel, they tend to discard the subgrid locations of the data. Use of a locally continuous function for the interpolation kernel can preserve the subgrid location information in the data, at the cost of numerical sensitivity to the spatial variation in data density. This paper suggests a simple numerical procedure, based on a single correlation coefficient parameter, to eliminate such numerical sensitivity.

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Assimilating XBT Data into HYCOM

Cited by 23 publications

References 30 publications

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

The Ensemble Kalman Filter: theoretical formulation and practical implementation

On “Gridless” Interpolation and Subgrid Data Density

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