# Evaluation of the Consistency of Three GRACE Gap-Filling Data
## 6 Conclusions  
The 11-month gap between the two missions inevitably limits our ability to systematically analyze and fully utilize the satellite observations of GRACE and GFO. In this paper, we scrutinized three gap-filling datasets. To verify the differences and applicability of the three types of gap-filling methods, we analyzed and compared the QF, IGG, and SSA data in the spectral and space domains. The main conclusions of the study are summarized below. (1) The SHCs of the QF, IGG, and SSA data are consistent up to degree 12. On the one hand, the SHCs of these three datasets show good consistency, with more than 80% of rRMS values <1 relative to Swarm. On the other hand, the global terrestrial EWH results based on the 12-order SHCs are still consistent in most parts of the world, especially in reflecting the signals of polar ice melting and strong terrestrial water storage variations. It is noticeable that there is better consistency between IGG and SSA due to the fact that both SSA and IGG data are highly inherited from GRACE/GFO. (2) The IGG and SSA data are basically consistent over the 11 gap months, with the signal intensity in IGG slightly higher than in SSA, but the IGG shows a faster increase in the mean ocean water mass and the SSA appears to better capture the interannual variation in the terrestrial water storage. (3) The major shortcomings of IGG data are mainly reflected in two aspects. First, as the IGG data use only six EOFs for GRACE at most, it may occasionally miss the inter-annual variation in local areas or fail to detect post-seismic adjustments of giant earthquakes. Second, the IGG recovers the time-varying gravity fields from the SLR using only GRACE data prior to August 2016, which causes the IGG to maintain the previous variation pattern during the extrapolation time period, so it may not accurately capture some sudden mass-changing signals after August 2016. (4) The lower-degree terms of the QF coefficients (l \(\leq\) 16) are essentially consistent with those of SSA and IGG, but the noise increases significantly for the high-order terms of the QF data (l \(>\) 16), so this solution is suitable only for large-scale mass migration studies. (5) Based on the triple collocation method, we propose a new scheme to derive a weight matrix that can fuse these three datasets into a more robust solution. Limited by the resolution of QF data, here we only fused the SHCs data up to degree 15, but they can be extended to higher degrees should higher-resolution data be released in the future. There are other uncertainties in the latest gap-filling data that should be noted. First, the gap-filling data should be carefully interpreted during transient climatic events. Either IGG or SSA may fail to capture changes in water storage caused by such events. Second, these three gap-filling datasets should be utilized with caution in regions with weak hydrological signals due to poor consistency in RMS and correlation coefficients. Third, due to the unexplained large discrepancies among the altimeter, Argo, and GRACE data, the filling data require careful interpretation when used to monitor the global mean sea level changes after 2016. Conceptualization, A.Q. and S.Y. ; methodology, A.Q. and S.Y. ; formal analysis, A.Q., F.L., B.S., and G.S. ; investigation, A.Q. and S.Y. ; writing--original draft preparation, A.Q., F.L., B.S., G.S., and X.L. ; writing--review and editing, S.Y. ; visualization, A.Q. ; supervision, S.Y. ; project administration, S.Y. ; funding acquisition, A.Q., S.Y., F.L., G.S., and B.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hebei Key Laboratory of Earthquake Dynamics Open Fund (grant no. FZ202214), the University of Chinese Academy of Sciences Research Start-up Grant (grant no. 110400M003), the Self-Funded Project of Scientific Research and Development Plan of Langfang Science and Technology Bureau (grant nos. 2020013045 and 2021011030), the Central University Basic Research Fund of China (grant nos.