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The error variance-covariance matrices of the monthly GRACE gravity field models are usually well-structured (e.g., order-leading) to contain the error information of the monthly gravity field models, and they are important information to further improve the accuracy of the estimated mass transportation by a post-processing scheme. Intensive studies have been performed to understand the impact of different approximations of the full error variance-covariance matrix on the mass estimates obtained with the conventional GRACE-post processing methods (e.g., the de-striping method). In this study, based on the variants of the mascon approach which treat monthly GRACE solutions as input, we consider four different structures to the error variance-covariance matrix, (i) full matrix, (ii) block diagonal matrix, (iii) diagonal matrix, and (iv) identity matrix, and examine their impact on the mass transport estimates in Greenland. Using both synthetic and real data, we analyze the results at four temporal scales: (i) the long-term decadal scale, (ii) the inter-annual scale, (iii) the seasonal scale, and (iv) the monthly scale. Based on the synthetic study, we find that for the recovery of the long-term trend, the application of the diagonal structure obtains the best estimates. This is caused by the amplification of the model error in the mascon approach when considering the full and block diagonal structures of error variance-covariance matrix, not due to any imperfection of them. Therefore, we emphasize that one should be aware of mascon model deficiency in the mascon approach. Furthermore, the best inter-annual, seasonal and monthly mass estimates are derived by considering the block diagonal and full structures. This is caused by the behavior of the bias and the unique parameterization error in the case of different structures. A similar finding is also presented in the real data case. Finally, our analysis denotes the necessity of releasing the block diagonal structure together with the official monthly gravity field model for the GRACE Follow-On mission, instead of releasing only the diagonal structure as done for the GRACE mission. [ABSTRACT FROM AUTHOR]

The problem of determining mass redistribution within the Earth system from time-variable gravity (TVG) data is non-unique. Over seasonal and decadal time-scales, mass redistribution likely takes place on the Earth's surface. By approximating the Earth's surface by a sphere, surface mass variation can be uniquely determined from TVG data. Recently, using the improved GRACE TVG data, Li et al. and Ditmar found that such spherical approximation is no longer tenable and suggested practical approaches to accommodate the elliptical shape of the Earth. In this study, we develop a rigorous method of determining surface mass change on the Earth's reference ellipsoid. We derive a unique one-to-one relationship between ellipsoidal spectra of surface mass and gravitational potential for the ellipsoidal geometry. In conjunction with our ellipsoidal formulation, the linear transformation between spherical and ellipsoidal harmonic coefficients of the geopotential field enables us to determine mass redistribution on the ellipsoid from GRACE TVG data. Using the Release 6 of GRACE TVG data to degree 60, we show that the ellipsoidal approach reconciles surface mass change rate significantly better than the spherical computation by 3–4 cm yr−1, equivalent to 10–15  per cent increase of total signal, in Greenland and West Antarctica. We quantify the spherical approximation error over the polar regions using GRACE Level-2 TVG data as well as mascon solutions, and demonstrate that the systematic error increases linearly with the maximum degree used for the synthesis. The terrestrial water storage computation is less affected by the spherical approximation because of geographic location of major river basins (lower latitude) and signal characteristics. The improvement of TVG data from GRACE and its Follow-On necessitates the ellipsoidal computation, particularly for quantifying mass change in polar regions. [ABSTRACT FROM AUTHOR]