Assimilation of dynamic topography in a global model

Absolute dynamic topography, i.e. the difference between time dependent multi-mission altimetric sea surface height and one of the most recent GOCE and GRACE based geoids, is assimilated in a global ocean general circulation model. To this end we apply an ensemble based Kalman technique, the "Error Subspace Transform Kalman Filter" (ESTKF). Here we present an update of our work. First of all the geoid is improved over previous versions. The ocean model now includes better dynamics and full sea-ice ocean interactions and more realistic surface forcing. Finally the assimilation method is augmented by a fixed lag smoother technique. This smoother allows to significantly improve the model performance, most strikingly in the first adjustment phase

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

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

Problems and new concepts in local geoid solutions

The local modeling of the gravity field has always been a theoretical challenge for geodesy, particularly at wavelengths comparable with the data window. Several solutions have been proposed all of which are known to leave long wavelength errors in the geoid. The current belief on the origin of this error is criticized. A new proposal, namely the use of the Slepian basis to overcome this difficulty, is illustrated in general terms