Estimating Geocenter Motion and Changes in the Earth's Dynamic Oblateness from a Statistically Optimal Combination of GRACE Data and Geophysical Models

Time-varying gravity field solutions of the GRACE satellite mission enable an observation of Earth's mass transport on a monthly basis since 2002. One of the remaining challenges is how to complement these solutions with sufficiently accurate estimates of very low-degree spherical harmonic coefficients, particularly degree-1 coefficients and C20. An absence or inaccurate estimation of these coefficients may result in strong biases in mass transports estimates. Variations in degree-1 coefficients reflect geocenter motion and variations in the C20coefficients describe changes in the Earth's dynamic oblateness (ΔJ2). In this study, we developed a novel methodology to estimate monthly variations in degree-1 and C20coefficients by combing GRACE data with oceanic mass anomalies (combination approach). Unlike the method by Swenson et al. (2008), the proposed approach exploits noise covariance information of both input datasets and thus produces stochastically optimal solutions. A numerical simulation study is carried out to verify the correctness and performance of the proposed approach. We demonstrate that solutions obtained with the proposed approach have a significantly higher quality, as compared to the method by Swenson et al. Finally, we apply the proposed approach to real monthly GRACE solutions. To evaluate the obtained results, we calculate mass transport time-series over selected regions where minimal mass anomalies are expected. A clear reduction in the RMS of the mass transport time-series (more than 50 %) is observed there when the degree-1 and C20 coefficients obtained with the proposed approach are used. In particular, the seasonal pattern in the mass transport time-series disappears almost entirely. The traditional approach (degree-1 coefficients based on Swenson et al. (2008) and C20 based on SLR data), in contrast, does not reduce that RMS or even makes it larger (e.g., over the Sahara desert). We further show that the degree-1 variations play a major role in the observed improvement. At the same time, the usage of the C20 solutions obtained with the combination approach yields a similar accuracy of mass anomaly estimates, as compared to the results based on SLR analysis. The computed degree-1 and C20 coefficients will be made publicly available.