Combining Stationary Ocean Models and Mean Dynamic Topography Data

In this study, a new estimate for the Mean Dynamic Topography (MDT) and its error description is analysed in terms of its impact on the performance of ocean models. For the first time, a full MDT error covariance matrix is available whose inverse can readily be used as weighting matrix in the optimization. Two different steady-state inverse ocean models are analysed in terms of their response to the new MDT data set. The output of each of these ocean models in turn provides a combined satellite-ocean model MDT. This study investigates whether the inverse ocean models benefit from the new MDT data set and its error covariance. It is examined whether oceanographic features such as the ocean current structure, the overturning circulation and heat transports are improved by the assimilated MDT data set. Special focus is given to the MDT error covariance estimate as it is crucial in the optimization

RIFUGIO

Some recently designed gravity field models together with their variance/ covariance information could possibly be integrated into geo-scientific process models. In case of stationary ocean models the ocean's mean dynamic topography (altimetric mean sea surface referenced to the geoid) is assimilated to improve estimates of the general ocean circulation. In our study we want to combine our complete gravity field models with altimetric data for which a full error propagation is implemented in the processing. Thus we derive estimates of the ocean's mean dynamic topography with a regular covariance matrix. The goal of this project is to integrate this information into stationary ocean models and to assess the effects of this data combination on improving ocean models. The study focuses on the North Atlantic Ocean bounded by the latitudes 30° S and 80(66)° N

RIFUGIO - Rigorous Fusion of Gravity Field into Stationary Ocean Models

So-called complete gravity field models together with their full error variance/covariance information have recently been designed to be integrated into geo-scientific process models. In our case, the ocean's mean dynamic topography (altimetric mean sea surface referenced to the geoid) is used to improve estimates of the general ocean circulation in the context of stationary ocean models. We want to combine complete gravity field models with altimetric data for which a full error propagation is also implemented in the processing. Thus we derive estimates of the ocean's mean dynamic topography with a regular covariance matrix. The goal of this project is to assess the effects of this data combination on improving ocean models. Preliminary results already show that geoid models developed from GRACE data are, while accurate on very long scales, hardly yet accurate enough for that purpose. We anticipate that the increased accuracy, especially on shorter scales, of gravity measurements from GOCE will contribute to a more realistic description of ocean currents as well as mass and heat transports

Kombination stationärer Ozeanmodelle mit Daten der Mittleren Dynamischen Topographie

In this study, a new estimate for the Mean Dynamic Topography (MDT) and its error description is analysed in terms of its impact on the performance of ocean models. For the first time, a full MDT error covariance matrix is available whose inverse can readily be used as weighting matrix in the optimization. Two different steady-state inverse ocean models are analysed in terms of their response to the new MDT data set. The output of each of these ocean models in turn provides a combined satellite-ocean model MDT. This study investigates whether the inverse ocean models benefit from the new MDT data set and its error covariance. It is examined whether oceanographic features such as the ocean current structure, the overturning circulation and heat transports are improved by the assimilated MDT data set. Special focus is given to the MDT error covariance estimate as it is crucial in the optimization