Abstract

Short term mass variations cannot be measured adequately by GRACE due to undersampling. Therefore, they are removed from the measurements beforehand using geophysical models (de-aliasing). As GRACE data analysis has not yet reached ultimate accuracy, one presumes that inadequate de-aliasing and geophysical model uncertainties are one possible reason for this. The standard GRACE de-aliasing procedure disregards geophysical model (atmospheric and oceanic) errors, therefore a procedure has been developed to take atmospheric and oceanic uncertainties into account. Thereby, we expect to improve the de-aliasing product and the resulting GRACE gravity field models. As the GRACE results are used for geophysical interpretations, any increase in accuracy will lead to a better understanding of geophysical processes. The paper summarizes the results of this work. First, the standard de-aliasing process as well as the new procedure, which is able to take geophysical model uncertainties into account, is summarized. After the definition of the performed error scenarios, an overview and discussion of the applied atmospheric and oceanic error maps is given. Finally, the impact of the previously defined error scenarios on the de-aliasing coefficients as well as on a GRACE gravity field solution is investigated. K-band range-rate satellite-to-satellite tracking (KBRR) residuals, as an intermediate gravity field result and gravity field solutions based on the applied error scenarios are analyzed. From the results obtained so far it can be concluded that GRACE results or rather KBRR residuals are sensitive to atmospheric and oceanic model errors. Depending on the error structure of the introduced model uncertainties, KBRR residuals could have been reduced. However, it has to be stated that with respect to the current GRACE error budget, atmospheric and oceanic model uncertainties seem not to play a prominent role in the accuracy of current GRACE gravity field solutions. Nevertheless, further and deeper analysis of the KBRR residuals is needed, as (positive) effects of model uncertainties are visible here.

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