Assimilation of data into an ocean model with systematic errors near the equator

Bell, Michael; Martin, Matthew J.; Nichols, Nancy K.

doi:10.1256/qj.02.109

Cited by 108 publications

(105 citation statements)

References 22 publications

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“…For this reason, many works point out that the model biases should be considered during the assimilation process Dee and Silva, 1998;Bell et al, 2004;Dee, 2005;Xie and Zhu, 2010). Also, improving the co-variances of the ensemble members may lead to more accurate analyses (Oke et al, 2008;Xie and Zhu, 2010).…”

Section: Adjustment Of the Altimetry And Velocity Fieldsmentioning

confidence: 99%

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: Adjustment Of the Altimetry And Velocity Fieldsmentioning

confidence: 99%

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

et al. 2015

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“…This fact limits the use of the proposed method for seasonal numerical experiments. To prevent the reduction of inter-annual variability it is also of interest to try more sofisticated correction methods such as (Bell et al, 2004).…”

Section: Comparison Of Model Results With Observationsmentioning

confidence: 99%

“…Dee and da Silva (1998) have applied this idea to numerical weather prediction. Bell et al (2004) built on the ideas of the latter work and applied a term of "pressure correction" combining it with the augmenting state vector concept.…”

Section: Developed a Continuous Variationalmentioning

confidence: 99%

Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model

et al. 2008

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Abstract. This study focuses on an accurate estimation of ocean circulation via assimilation of satellite measurements of ocean dynamical topography into the global finite-element ocean model (FEOM). The dynamical topography data are derived from a complex analysis of multi-mission altimetry data combined with a referenced earth geoid. The assimilation is split into two parts. First, the mean dynamic topography is adjusted. To this end an adiabatic pressure correction method is used which reduces model divergence from the real evolution. Second, a sequential assimilation technique is applied to improve the representation of thermodynamical processes by assimilating the time varying dynamic topography. A method is used according to which the temperature and salinity are updated following the vertical structure of the first baroclinic mode. It is shown that the method leads to a partially successful assimilation approach reducing the rms difference between the model and data from 16 cm to 2 cm. This improvement of the mean state is accompanied by significant improvement of temporal variability in our analysis. However, it remains suboptimal, showing a tendency in the forecast phase of returning toward a free run without data assimilation. Both the mean difference and standard deviation of the difference between the forecast and observation data are reduced as the result of assimilation.

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“…Troccoli et al (2002) reported that the univariate assimilation of temperature profiles, without attempting to correct salinity, can induce spurious convection and result in errors in the subsurface temperature and salinity fields. Bell et al (2004) found that assimilation of temperature data into an ocean model near the equator often results in a dynamically unbalanced state with unrealistically strong vertical motions that corrupt the increments introduced by the data assimilation scheme and lead to an ocean state with a biased density field. To address these problems recent developments have focused on dynamically balanced multivariate analysis schemes for ocean data assimilation.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Troccoli et al (2002) proposed a method to preserve water mass characteristics, and Burgers et al (2002) suggested imposing geostrophically balanced increments to horizontal velocities and density. In addition, various bias estimation and treatment techniques, inspired by Dee and Da Silva (1998), have been proposed for ocean data assimilation (Bell et al 2004;Chepurin et al 2005;Balmaseda et al 2007). However, most of these techniques are not truly multivariate, since the analysis is performed by taking dynamical constraints that are applied a posteriori to a statistically generated univariate analysis.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Ocean Data Assimilation System for Seasonal Prediction

Yin

Alves

Oke

2011

Monthly Weather Review

127

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A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts that are forced with perturbed surface fluxes. The small ensemble is augmented with multiple small ensembles from previous assimilation cycles, yielding a larger ensemble that consists of perturbed forecasts from the last month. This larger ensemble is used to represent the system's time-dependent background error covariance. At each assimilation cycle, a central analysis is computed utilizing the ensemble-based covariance. Each of the perturbed ensemble members are nudged toward the central analysis to control the ensemble spread and mean. The ensemble-based covariances generated by PEODAS potentially yield dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for example during El Niñ o and La Niñ a conditions. These differences are explored in terms of the dominant dynamics and the system's errors. The performance of PEODAS during a 27-yr reanalysis is evaluated through a series of comparisons with assimilated and independent observations. When compared to its predecessor, POAMA version 1, and a simulation with no assimilation of subsurface observations, PEODAS demonstrates a quantitative improvement in skill. PEODAS will form the basis of Australia's next operational seasonal prediction system.

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Assimilation of data into an ocean model with systematic errors near the equator

Cited by 108 publications

References 22 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

Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model

An Ensemble Ocean Data Assimilation System for Seasonal Prediction

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