Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Abstract

Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R2) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community.