Since their development in the 1970s Pressure Inverted Echo Sounders (PIES) have been used to address numerous oceanographic questions. PIES measure ocean bottom pressure and acoustic round trip travel time which is a vertically integrated function of density. Since 2003 the Alfred Wegener Institute (AWI) operates an array of six PIES along the Good Hope line south of Africa. The Good Hope line is a ground track of satellite altimeter Jason 1 and 2 across the Antarctic Circumpolar Current (ACC) and the PIES were deployed at cross over points of the ascending and descending track. The rst part of this thesis uses the Gravest Empirical Mode (GEM) method to derive Sea Surface Height (SSH) anomalies and baroclinic ACC transport. The derived total SSH anomalies were compared to two different satellite altimetry product. The AVISO product is a smoothed and gridded combination of data from different satellites while the openADB database provides the along track measured data without any smoothing or gridding. The correlation of the total (broclinic+barotropic) SSH anomaly with satellite altimetry results higher correlation coecients for the gridded AVISO product (0.33-0.92) compared to the along track openADB (0.24-0.92) product. Dividing the total SSH anomaly into baroclinic and barotropic part results a contribution of the barotropic component in the order of 30-60%. The highest barotropic components are found inbetween the fronts. Calculating the baroclinic ACC transports results a mean of 147± 2.4 Sv for the deployment period 2007-2008 and 142±1.9 Sv respectively for the period 2008-2010. Both the mean and standard deviation compare well with previous observations and model results. In conclusion the PIES derived SSH anomaly showed a significant contribution of the barotropic component to the total variability and a better correlation with the (smoother) gridded satellite altimetry product. The derived baroclinic ACC transport is in close agreement with previous measurements. It canbe derived with higher temporal resolution than in previous studies. The second part of this thesis investigates how insitu ocean bottom pressure (OBP) can improve a least square inversion of GPS (Global Positioning System) data, GRACE (Gravity Recovery and Climate Experiment ) data and modeled OBP used to derive global ocean mass changes. The inversion combines the information provided the different datasets and ts a mathematical model through it. The difference between the model and the data is minimized in a least square sense. The inversion with in-situ OBP locally improves the correlation with the Bottom Pressurerecorders (BPRs) but does only slightly in uence global parameters like the global mean ocean mass or the geocenter motion. In conclusion there are to less BPRs which are furthermore irregular distributed in time and space to significantly improve the inversion.