Impact of combining GRACE and GOCE gravity data on ocean circulation estimates

Janjić, Tijana; Schröter, Jens; Savcenko, R; Bösch, Wolfgang; Albertella, A.; Rummel, Reiner; Klatt, Olaf

doi:10.5194/os-8-65-2012

Cited by 19 publications

(10 citation statements)

References 42 publications

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“…The LETKF, proposed by Hunt et al (), is an advanced ensemble data assimilation method. The ensemble methods have been successfully applied to various models including global and regional NWP models (Houtekamer and Mitchell, ; ; Dowell et al ; Snyder and Zhang, ; Miyoshi and Yamane, ; Lange and Janjić, ; Schraff et al ) as well as ocean models (Haugen and Evensen, ; Penduff et al ; Brusdal et al ; Brankart et al ; Skachko et al ; Janjić et al , b).…”

Section: Local Ensemble Transform Kalman Filtermentioning

confidence: 99%

Study of conservation laws with the Local Ensemble Transform Kalman Filter

Zeng

Janjić

2016

Quart J Royal Meteoro Soc

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Numerical discretization schemes have a long history of incorporating the most important conservation properties of the continuous system in order to improve the prediction of the nonlinear flow. The question arises whether data assimilation algorithms should follow a similar approach. To address this issue, we explore the conservation properties during data assimilation using perfect model experiments with a 2D shallow‐water model preserving important properties of the true nonlinear flow. The data assimilation scheme used here is the Local Ensemble Transform Kalman Filter with varying observed variables, inflation, localization radius and thinning interval. It is found that, during the assimilation, the total energy of the analysis ensemble mean converges with time towards the nature run value. However, enstrophy, divergence and the energy spectra are strongly affected by the data assimilation settings. Having in mind that the conservation of both the kinetic energy and enstrophy by the momentum advection schemes in the case of non‐divergent flow prevents a systematic and unrealistic energy cascade towards the high wave numbers, we test the effects on the prediction depending on the type of error in the initial condition. During the assimilation, we assess the downward nonlinear energy cascade through a scalar, domain‐averaged noise measure. We show that the accumulated noise during assimilation and the error of analysis are good indicators of the quality of the prediction.

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Section: Local Ensemble Transform Kalman Filtermentioning

confidence: 99%

Study of conservation laws with the Local Ensemble Transform Kalman Filter

Zeng

Janjić

2016

Quart J Royal Meteoro Soc

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“…Since the DOT data does not include a specification of observation errors, we assume a constant of 5 cm (including representativeness error) consistent with our earlier studies (e.g. Janjić et al 2012b). We did not attempt to apply a spatially variable representation errors, which might be estimated from sea-level anomalies (see Sakov et al 2012), because of our assimilation of absolute DOT.…”

Section: Configuration Of Assimilation Systemmentioning

confidence: 99%

On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state

Androsov

Nerger

Schnur³

et al. 2018

J Geod

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General ocean circulation models are not perfect. Forced with observed atmospheric fluxes they gradually drift away from measured distributions of temperature and salinity. We suggest data assimilation of absolute dynamical ocean topography (DOT) observed from space geodetic missions as an option to reduce these differences. Sea surface information of DOT is transferred into the deep ocean by defining the analysed ocean state as a weighted average of an ensemble of fully consistent model solutions using an error-subspace ensemble Kalman filter technique. Success of the technique is demonstrated by assimilation into a global configuration of the ocean circulation model FESOM over 1 year. The dynamic ocean topography data are obtained from a combination of multi-satellite altimetry and geoid measurements. The assimilation result is assessed using independent temperature and salinity analysis derived from profiling buoys of the AGRO float data set. The largest impact of the assimilation occurs at the first few analysis steps where both the model ocean topography and the steric height (i.e. temperature and salinity) are improved. The continued data assimilation over 1 year further improves the model state gradually. Deep ocean fields quickly adjust in a sustained manner: A model forecast initialized from the model state estimated by the data assimilation after only 1 month shows that improvements induced by the data assimilation remain in the model state for a long time. Even after 11 months, the modelled ocean topography and temperature fields show smaller errors than the model forecast without any data assimilation.

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“…Existing studies regarding dynamic topography and ocean circulation estimates from GOCE [e.g., Knudsen et al, 2011;Bingham et al, 2011;Albertella et al, 2012;Janjić et al, 2012] as well as pre-GOCE studies [e.g., Andersen and Knudsen, 2009;Maximenko et al, 2009;Rio et al, 2011] make use of special filter techniques to homogenize the different information and to combine a gravity field model with altimetric-derived sea surface heights. No or only insufficient information about the variances and especially the covariances are provided with these dynamic topography models.…”

Section: Introductionmentioning

confidence: 99%

Mean dynamic topography estimates purely based on GOCE gravity field models and altimetry

Becker

Brockmann

Schuh

2014

Geophys. Res. Lett.

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Citation:Becker, S., J. M. Brockmann, and W.-D. Schuh (2014), Mean dynamic topography estimates purely based on GOCE gravity field models and altimetry, Geophys. Res. Lett., 41, 2063Lett., 41, -2069Lett., 41, , doi:10.1002 Abstract The quality of mean dynamic topography (MDT) models derived from an altimetric mean sea surface and a gravity field model mainly depends on the spatial resolution and accuracy of the particular gravity field model. We use an integrated approach which allows for estimating the MDT and its (inverse) covariance matrix on a predefined grid which is one of the requirements for ocean data assimilation. The quality and accuracy of the MDT directly reflects the quality and accuracy of the used gravity field model. For the first time, MDT estimates along with its full error covariance matrix based on Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) data can be provided. We demonstrate the progress accomplished with GOCE processing and the valuable contribution of the GOCE gravity field models regarding the estimation of the MDT by showing results based on altimetric observations of Jason-1 and Envisat in combination with different GOCE gravity field models for the North Atlantic.

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Impact of combining GRACE and GOCE gravity data on ocean circulation estimates

Cited by 19 publications

References 42 publications

Study of conservation laws with the Local Ensemble Transform Kalman Filter

Study of conservation laws with the Local Ensemble Transform Kalman Filter

On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state

Mean dynamic topography estimates purely based on GOCE gravity field models and altimetry

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