A New GRACE Filtering Approach Based on Iterative Image Convolution

Abstract

AbstractThe Gravity Recovery and Climate Experiment (GRACE) mission and its successor GRACE Follow‐On (GRACE‐FO) mission provide humans with a unique perspective on mass distribution through accurate temporal gravity field models. However, these models suffer from unavoidable stripe errors in the spatial domain. A new approach based on image processing has been developed for filtering GRACE and GRACE‐FO spherical harmonic solutions. In this approach, the gravity fields are treated as noise‐contaminated images, and the stripe errors are expected to be suppressed by convolving the input with an appropriate kernel. To verify the effectiveness of the proposed approach, a closed‐loop simulation environment over the nominal mission lifetime of 5 years is designed. Image processing indicators and geophysical information evaluation results demonstrate the superior performance of the new approach in suppressing stripe errors. In the context of real GRACE data processing, our new approach still yields better results as follows: (a) The comprehensive ability of de‐striping and retaining details has slight advantages over some commonly used filters, as demonstrated through spatial distributions, amplitude spectra, and coefficient spectra. (b) The equivalent water height time series at the basin scale is comparable to the results derived from other filters or Level‐3 products. (c) The estimated signal amplitude of the Mw9.1 2004 Sumatra earthquake co‐seismic gravity change is 52.7% larger than that derived from the Gaussian 300 km filter, which is much closer to the theoretical value derived from the dislocation theory.