An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

Xie, Jiping; Counillon, François; Zhu, Jiang; Bertino, Laurent

doi:10.5194/os-7-609-2011

Cited by 54 publications

(35 citation statements)

References 57 publications

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“…They were selected from the model free run for each assimilation day, regarding the intra-seasonal variability and the high-frequency model dynamics. This number of ensemble members was chosen after a few sensitivity experiments considering a reasonable representation of the model's anomalies without high computational cost, and is in agreement with the numbers used in recent works (e.g., Counillon and Bertino, 2009;Xie and Zhu, 2010;Xie et al, 2011).…”

Section: Generation Of a Running Ensemblementioning

confidence: 99%

“…It is already verified that the EnOI is able to effectively constrain the model towards observations. For instance, it was successfully applied to assimilate Argo data in the Pacific Ocean , sea level anomaly (SLA) data in the South China Sea (Xie et al, 2011) and in the Gulf of Mexico (Counillon and Bertino, 2009), and SLA, SST and Argo data in the Australian region (Oke et al, 2008) and in the Indo-Pacific Ocean .…”

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: Generation Of a Running Ensemblementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…Besides the window length, the choice of a background error covariance matrix is also a critical aspect in a data-scarce area such as the Arctic. The background error covariance used in an ocean data assimilation system can be -by increasing order of complexity -based on fixed multivariate spatial statistics (Cummings et al, 2009), an empirical estimation by a time-invariant ensemble or a seasonally variable ensemble (Brasseur et al, 2005;Xie et al, 2011). In the case of ice-ocean systems, sea ice data assimilation often relies on rudimentary ice-only nudging methods (Schweiger et al, 2011;Tietsche et al, 2013); however, the possibility to account for flow-dependent coupled ice-ocean data assimilation updates has already been demonstrated in Lisaeter et al (2003).…”

Section: Introductionmentioning

confidence: 99%

Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

Xie¹,

Bertino²,

Counillon³

et al. 2017

Ocean Sci.

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Abstract. Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991-2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging. IntroductionThe Arctic Ocean plays an important role in the global climate system, where the sea ice at the interface between atmosphere and ocean regulates the fluxes of heat, moisture and momentum. The recent warming of the Arctic and the change of its water cycle has been linked to the following manifestations: a significant reduction and thinning of the sea ice cover (Johannessen et al., 2004;Shimada et al., 2006;Rothrock et al., 2008;Kwok and Rothrock, 2009), more freshwater in the Arctic in the 2000s (Haine et al., 2015) and more mobility and faster deformations of the Arctic sea ice (Rampal et al., 2009;Spreen et al., 2011). The interpretation of such changes is severely hampered by the sparseness of the concerned observations, which should not be improved dramatically in the near future. It can be assisted by free-running model simulations, but those are usually hampered by mislocations of ice edge and certain water masses. One possibility is to study surrogate locations where similar processes are assumed to take place. Another solution is to correct the dynamical model by assimilating observations available over relevant timescales.The latter activities thu...

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“…The 4D-Var produces the best estimate over a finite time interval by minimizing a cost function considering the trajectory of the non-linear model and its linearized adjoint, as well as all observations available in the period. They have a relatively high computational cost (Evensen 2003;Moore et al 2011b;Xie et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

Costa

Tanajura

2015

Journal of Operational Oceanography

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Argo and along-track sea-level anomaly (SLA) data from satellites were assimilated into the Hybrid Coordinate Ocean Model in the western tropical and South Atlantic Ocean. An optimal interpolation method was employed in the assimilation, and the Cooper and Haines scheme projected the altimetry information into the subsurface. The run with assimilation of SLA and Argo data reduced the root mean square deviation of the 24 h forecasts of SLA, sea surface temperature, and subsurface temperature and salinity by 21.4%, 11.5%, 28.1%, and 15.8%, respectively, with respect to the control run without assimilation. Important improvements were also observed in the circulation.

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An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

Cited by 54 publications

References 57 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

Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

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