a b s t r a c tSatellite altimetry and GRACE observations carry both the signature of ocean tides and have in general complementary potential to resolve tidal constituents. It is therefore straightforward to perform a combined estimation of a global ocean tide model based on these two data sources. The present paper develops and applies a three step procedure for generating such a combined ocean tide model. First, the processing of multi-mission altimetry data is described along with the harmonic analysis applied to derive initially a pure empirical ocean tide model. Then the capability of GRACE to sense particular tidal constituents is elaborated and an approach to estimate tidal constituents from GRACE is outlined. In a third step a combination strategy with optimal stochastic data treatment is developed and applied to the altimetry-only tide model EOT08a and four years of GRACE observations, leading to the combined model EOT08ag. The differential contributions of GRACE to EOT08ag remain small and are mainly concentrated to the Arctic Ocean, an area with little or poor altimetry data. In comparison with other tide models, EOT08ag is validated by K-band range residuals, the impact on gravity field modelling and on precise orbit determination and by variance reduction of crossover differences. None of these comparison exhibits a significant improvement over the altimetry-only tide model except for a few areas above 60 • N. Overall the improvements of the combination remain small and appear to stay below the current GRACE baseline accuracy.
P>Improvements of a global state-of-the art ocean tide model are identified and quantified by applying three independent approaches, namely (i) empirical ocean tide analysis of multimission altimeter data, (ii) evaluation of GRACE data and gravity field models and (iii) high resolution hydrodynamic modelling. Although these approaches have different capabilities to sense ocean tides they obtain results which are basically consistent one with each other. The analysis of altimeter data clearly identifies significant residual amplitudes over shallow water for all major diurnal and semidiurnal constituents and the non-linear tide M-4. GRACE data and the time-series of monthly gravity field models exhibit-on a larger scale-residual ocean tide signals over much the same areas. The analysis of dynamic residuals of hydrodynamic modelling with data assimilation proves the validity of linear dynamics in the deep ocean and shows correlation of dynamic residuals with energy dissipation in these areas
SUMMARY A mean dynamic ocean topography (MDT) has been computed using a high resolution GOCE (Gravity field and steady‐state Ocean Circulation Explorer) gravity model and a new mean sea surface obtained from a combination of satellite altimetry covering the period 1992 October till 2010 April. The considered gravity model is GO‐CONS‐GCF‐2‐TIM‐R3, which computes geoid using 12 months of GOCE gravity field data. The GOCE gravity data allow for more detailed and accurate estimates of MDT. This is illustrated in the Southern Ocean where the commission error is reduced from 20 to 5 cm compared to the MDT computed using the GRACE gravity field model ITG‐Grace2010. As a result of the more detailed and accurate MDT, the calculation of geostrophic velocities from the MDT is now possible with higher accuracy and spatial resolution, and the error estimate is about 7 cm s−1 for the Southern Ocean.
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.
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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