The XGM2016 model is parameterized as a spherical harmonic series expansion resolved to degree and order (d/o) 719, which is the maximum resolution supported by the 15′ terrestrial gravity grid and a satellite-only model GOCO05s. For XGM2016, a signiﬁcant focus is the optimal combination of the new terrestrial data with the latest satellite gravity information. The combination is based on the rigorous solution of a full normal equation system up to the maximum d/o 719. The calculation of the XGM2019 spheroidal harmonic model coefficients up to d/o 719 consists of a weighted least squares adjustment of GOCO06s with the primary 15′ NGA ground gravity dataset. The XGM2016 and XGM2019 models used the 15′ NGA ground gravity dataset, however, the refined GFMs in this study are obtained by combining the GRACE/GOCE-based GGMs and EGM2008 model, The gravity field information of 5′ terrestrial gravity data in EGM2008 is fully utilized. In addition, to consider the influence of higher frequency gravity field signals caused by topography, the RTM is utilized to further compensate for the omission errors in the refined GGMs. In this study, the combination of the satellite-only GGM with the EGM2008 is based on a pure complementation of the spherical harmonic coefﬁcients at a speciﬁc degree, We added comparisons for XGM2016 or XGM2019 models in Table 4, we can find that the refined GFMs outperform XGM2016 and XGM2019 as well, the major improvement of the refined GFMs can be attributed to the GOCE data and topography signals. Please refer to page 14, Line 435-457. Table 4. Statistics of the height anomaly differences between GNSS/levelling and six higher-degree GFMs. Unit: (m).
What is the main difference (except of the terrestrial data over China) to other combined GFMs, such as XGM2016 or XGM2019? What is the main methodological difference and advantage of your method? Please comment on that question quantitatifely and qualitatively.