OPTIMIZED SIGNAL DENOISING AND ADAPTIVE ESTIMATION OF SEASONAL TIMING AND MASS BALANCE FROM SIMULATED GRACE-LIKE REGIONAL MASS VARIATIONS

Abstract

The gravity recovery and climate experiment (GRACE) satellite mission has been providing near-continuous measurements of Earth's mass variations at regional spatial scales since early 2003, with applications to hydrologic, oceanographic, and cryospheric research. Motivated by recent regional land ice solutions, we analyze an array of simulated GRACE-like signals and seek optimal procedures to denoise the time series and accurately determine the seasonal timing and the corresponding seasonal and net mass balances. For the purpose of signal denoising we consider Gaussian smoothing, wavelet thresholding, the ensemble empirical mode decomposition (EEMD), the complete EEMD with adaptive noise (CEEMDAN), and a Wiener filter. We achieve the best denoising performance with a Wiener filter where the signal and noise spectra are estimated with Gaussian smoothing and the highest frequency wavelet coefficients, respectively. For the purpose of estimating seasonal timing we consider wavelet multiresolution analysis, EEMD, CEEMDAN, and a new cluster analysis of the ensemble of seasonal intrinsic mode functions that result from executing the EEMD and CEEMDAN. We select CEEMDAN cluster analysis as the best approach due to its consistent performance and ability to provide reliable uncertainties. Lastly, we investigate the effect of signal noise, high-frequency signal power, and data gaps, on the accuracy of the estimated parameters.