Global gravity field models from GOCE applying the time-wise method (Invited)

Details Global gravity field models from GOCE applying the time-wise method (Invited) Global gravity field models from GOCE applying the time-wise method (Invited)

Dec 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufm.g33b..03p&link_type=abstract

American Geophysical Union, Fall Meeting 2010, abstract #G33B-03

Physics

Geophysics

[1214] Geodesy And Gravity / Geopotential Theory And Determination, [1240] Geodesy And Gravity / Satellite Geodesy: Results

Scientific paper

Based on several months of GOCE orbit and gradiometer data, optimum high-resolution global gravity field models have been derived. Global GOCE gravity field solutions are routinely processed in the frame of the ESA-funded project “GOCE High-level Processing Facility”. One of the key features of this model is that it is a GOCE-only solution in rigorous sense, i.e., no static a priori gravity field model has been used, neither as a background model, nor to constrain the solution. Thus the pure contributions to global gravity field models achievable from GOCE data can be evaluated, and special emphasis will be given the advantages of such GOCE-only models for a variety of applications, e.g. in oceanography and geophysics. In the presentation, the specific key features of the time-wise gravity field solution strategy, such as the involved functional and stochastic models, the optimum combination of the two normal equation systems related to the GPS-based precise kinematic GOCE orbit and gravity gradiometry, as well as the optimum treatment of the polar gap problem by means of several regularization strategies, are discussed. The new solution will be validated against existing global satellite-only and combined gravity field models, but also GPS/levelling observations in selected regions. From the comparison with the two-month solution which was presented at the ESA Living Planet symposium, the gain of processing longer data periods will be assessed, which will allow consolidated performance predictions for the future.

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