# Evaluation of the Consistency of Three GRACE Gap-Filling Data
## 5 Discussion on Dataset Selection  
There were three SHC gap-filling products chosen here for systematic analysis and comparison. QF is derived from HLSST tracking and SLR data, which can be considered real observations. When there are no other data available, it can be a good candidate to fill the gaps between the GRACE and GFO periods. However, due to the limitation of the observation technology, the effective resolution of QF is currently limited to a maximum of \(\sim\)1000 km, which corresponds to the SHC degree 20 [31, 55]. IGG is derived from SLR data using the GRACE empirical orthogonal function decomposition model, which can estimate the same spatial resolution gravity fields as GRACE [16]. Considering only GRACE data  
Figure 12: Time series of selected coefficients of the SSA, QF, IGG, and the fused results. The gray bars indicate the location of the missing data of GRACE. (**a**–**n**) denote the C\({}_{2,1}\) to C\({}_{15,1}\) time series, respectively.  
prior to August 2016 are used in IGG and only six EOFs for GRACE are used at most, it may result in partial loss of actual signals and increasing uncertainties after August 2016. SSA is a purely data-driven approach fully inherited from GRACE/GFO observations, so it is easy to implement and can maintain the spatial resolution of GRACE.  
Since these three types of data are produced by different approaches, they may be applicable to different application fields. Obviously, we cannot simply determine which data are better or worse but need to analyze the data in a comprehensive manner. As mentioned in Section 3, we focused on several topical issues: Changes in terrestrial water storage, polar ice melting, global mean sea level change, the co-seismic and post-seismic signals, and sudden hydrological events. First, all these data have the ability to monitor changes in strong terrestrial water storage (Figure 4d) and polar ice melting (Figure 4a,b) but should be utilized with caution in regions with weak hydrological signals (Figure 7). Second, QF is suitable only for large-scale mass migration studies due to significant noise in high-order terms (Figure 3), but it can be a good candidate to fill the gaps between GRACE and GFO if no other data are available. Third, since only GRACE data prior to August 2016 were used in IGG [16], IGG may not accurately capture some sudden mass-changing signals after August 2016. Therefore, SSA is more apt at producing GRACE-like results when monitoring these signals after 2017 (Figure 4c,e). Fourth, as the IGG data use only six EOFs for GRACE at most, they may occasionally miss the inter-annual variation in local areas. So SSA may be more reliable to monitor hydrological signals (Figure 10). However, it should be noted that SSA is entirely data-driven rather than actually observed [21], and it occasionally fails to capture sudden climatic events during data gaps (Figure 10d), so we need to use these data with caution as well.  
To reduce the frequency of random errors and outliers in the gap-filling product, we proposed a new scheme to fuse these three datasets based on the TC method. The fusion results suppress the signal noise while maintaining the seasonal and interannual variation patterns in the gap months. The fusion results may not always be the best, but they ensure consistently acceptable performance. Therefore, when it is difficult to determine which data are more appropriate, we recommend the easy-to-implement fusion results.