Filling the gap between GRACE and GRACE follow-on observations based on principal component analysis

• Published in: Geophysical journal international 2024-03, Vol.236 (3), p.1216-1233
• Main Authors: Gu, Yanchao, Huang, Feilong, Huang, Jun, Yuan, Hongbo, Yu, Bing, Gao, Chongqin
• Format: Article
• Language: English
• ISSN: 0956-540X; 1365-246X
• DOI: 10.1093/gji/ggad484

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On (GRACE-FO), have revolutionized the approach to monitoring global mass variations. However, the presence of several gaps, notably the continuous 11-month gap between the two missions, has generated a disruption in observations and hindered the analysis and application of the data. To address this problem, we have proposed a spectral domain gap-filling approach based on principal component analysis (PCA). Our simulation experiments demonstrate that the PCA gap-filling technique has significant potential to successfully reconstruct global mass variation and accurately capture real signals for most basins with an accuracy of less than 2 cm. When applied to actual missing data, our methodology delivers highly consistent results with previously published filling approaches, such as singular spectrum analysis and improved multichannel singular spectrum analysis method, for most of the global basins. Noteworthy, in the case of the Nelson basin, our PCA gap-filling method outperforms other methods in capturing seasonal signals and the return to a normal level of the terrestrial water storage changes in 2018. A comparison in the spectral domain indicates that the accuracy of the PCA-filling output is comparable to the original GRACE(-FO) data. Moreover, our method exhibits high generality, allowing for direct application to continuous GRACE(-FO) data without other additional data processing and without differentiating the types of missing data. Therefore, the proposed PCA gap-filling method offers exciting opportunities to guarantee the continuity of global mass change observations and benefit subsequent applications that require continuous data records.