Estimating a Mean Ocean State from Hydrography and Sea-Surface Height Data with a Non-linear Inverse Section Model: Twin Experiments with a Synthetic Dataset


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mlosch [ at ] awi-bremerhaven.de

Abstract

The recovery of the oceanic flow field from in situ data is one ofthe oldest problems of modern oceanography. In this study, astationary, non-linear inverse model is used to estimate a meangeostrophic flow field from hydrographic data along a hydrographicsection. The model is augmented to improve these estimates withmeasurements of the absolute sea-surface height by satellitealtimetry. Measurements of the absolute sea-surface height includeestimates of an equipotential surface, the geoid. Compared tooceanographic measurements, the geoid is known only to low accuracyand spatial resolution, which restricts the use of sea-surface heightdata to applications of large scale phenomena of the circulation.Dedicated satellite missions that are designed for high precision,high resolution geoid models are planned and/or in preparation. Ourstudy, which relies on twin experiments, assesses the importantcontribution of improved geoid models to estimating the mean flowfield along a hydrographic section. When the sea-surface height dataare weighted according to the error estimates of the future highlyaccurate geoid models GRACE (Gravity Recovery And Climate Experiment)and GOCE (Gravity field and steady-state Ocean Circulation Explorer)integrated fluxes of mass and temperature can be determined with anaccuracy that is improved over the case with no sea-surface heightdata by up to 55%. With the error estimates of the currently bestgeoid model EGM96, the reduction of the estimated flux errors does notexceed 18%.



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Scopus/ISI peer-reviewed
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Published
Eprint ID
4408
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Losch, M. , Redler, R. and Schröter, J. (2002): Estimating a Mean Ocean State from Hydrography and Sea-Surface Height Data with a Non-linear Inverse Section Model: Twin Experiments with a Synthetic Dataset , Journal of physical oceanography, 32 , pp. 2096-2112 .


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