Filtering GRACE temporal gravity field solutions using ensemble empirical mode decomposition approach - Huan, Changmin; Wang, Fengwei; Zhou, Shijian; Qiu, Xiaomeng (Frontiers in Earth Science, 2023)
Due to the strong noise that exist in GRACE (Gravity Recovery and Climate Experiment) temporal gravity field solutions, geophysical signals are normally drowned which need many effective filtering approaches. Considering the advantage of the ensemble empirical mode decomposition (EEMD) approach, we first used the EEMD to filter the noise in this study together with the EMD for comparisons. In full consideration of that the lower degree the noise weaker, we divide the gravity field solutions into two parts (degree 2-28 and degree 29-60) based on the ratios of the latitude-weighted RMS over the land and ocean signals when adopting different truncated degrees. In the processing, we considered the problem of the endpoint effect and adopted the autoregressive model approach for an extension. Finally, we effectively compared it with the EMD (Empirical mode decomposition) method and the results show that the fitting error after EEMD filtering is almost always smaller than that after EMD filtering, and the mean ratio of root mean square (RMS) after EEMD filtering is larger than that EMD filtering, which are 3.45 and 3.40 for EEMD and EMD, respectively, indicating that EEMD approach performs better than EMD for filtering GRACE solutions, and retains more geophysical signals with less signal leakage.