Separation and Recovery of Geophysical Signals Based on the Kalman Filter with GRACE Gravity Data

Xiaolong Wang,Zhicai Luo,Hao Zhou,Bo Zhong,Yihao Wu,Qiong Li,Zhengkai Huang

doi:10.3390/rs11040393

Abstract

Monthly gravitational field solutions as spherical harmonic coefficients produced by the GRACE satellite mission require post-processing to reduce the effects of shortwave-length noises and north–south stripe errors. However, the spatial smoothing and de-striping filter commonly used in the post-processing step will either reduce spatial resolution or remove short-wavelength features of geophysical signals, mainly at high latitudes. Here, by using prior covariance information that reflects the spatial and temporal features of the geophysical signals and the correlated errors derived from the synthetic model, together with the covariance matrix of the formal errors for the monthly gravity spherical harmonic coefficients, we apply the Kalman filter to separate the geophysical signal from GRACE Level-2 data and simultaneously to estimate the correlated errors. By increasing the number of observations, the iterative process is applied to update the state vector and covariance in the Kalman filter because the prior information is not accurate. Due to the inevitable truncation error, multiple gridded-gain factors method considering different temporal frequencies has been developed to recover the geophysical signal. The results show that the Kalman filter can reduce the high-frequency noises and correlated errors remarkably. When compared with the commonly used filter, no spatial filter (such as Gaussian filter) is used in the Kalman filter. Therefore, the estimated signal preserves its natural resolution, and more detailed information is retained. It shows good consistency when compared with mascon solutions in both secular trend and annual amplitude.

Highlights

After more than 15 productive years in orbit, the U.S./German Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided unprecedented insights into how our planet is changing by tracking the continuous movement of liquid water, ice, and the solid Earth [1]
In order to evaluate the performance of the methods as described above, we used the Kalman filter and multiple gridded-gain factors to process the monthly GRACE Level-2 data
The data sets provided by GFZ, JPL, and CSR start in April 2002 and end in June 2017, but the data sets provided by ITSG-Grace2014 lack a substantial amount of the month, so we selected the monthly data start in January 2003 and end in December 2014 from the four GRACE analysis centers above

Summary

Introduction

After more than 15 productive years in orbit, the U.S./German Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided unprecedented insights into how our planet is changing (mainly the surface mass variations) by tracking the continuous movement of liquid water, ice, and the solid Earth [1]. The first one is the mascon approach, directly using the intersatellite range-rate and acceleration observation to estimate the mass changes of mass concentration blocks (or “mascons”) on the Earth’s surface [2,3,4]. These mascon solutions are computed in presence of regularization constraint and no additional smoothing or empirical de-striping or filtering is applied. The direct use of GRACE Level-2 data to measure the surface mass changes of the Earth is affected by different kinds of noises, and if they are not filtered in the post processing, it is difficult to extract useful geophysical signals from the Level-2 data

Methods

Results

Discussion

Conclusion