Decorrelation of GRACE Time Variable Gravity Field Solutions Using Full Covariance Information

Alexander Horvath,Michael Murböck,Martin Horwath,Roland Pail

doi:10.3390/geosciences8090323

Abstract

In this study the feasibility and performance of time variable decorrelation (VADER) filters derived from covariance information on decadal Gravity Recovery and Climate Experiment (GRACE) time series are investigated. The VADER filter is based on publicly available data that are provided by several GRACE processing centers, and does not need its own Level-2 processing chain. Numerical closed loop simulations, incorporating stochastic and deterministic error budgets, serve as basis for the design of the filter setup, and the resulting filters are subsequently applied for real data processing. The closed loop experiments demonstrate the impact of temporally varying error and signal covariance matrices that are used for the design of decorrelation filters. The results indicate an average reduction of cumulative geoid height errors of 15% using time-variable instead of static decorrelation. Based on the simulation experience, a real data filtering procedure is designed and set up. It is applied to the ITSG-Grace2014 time variable gravity field time series with its associated full monthly covariance matrices. To assess the validity of the approach, linear mass trend estimates for the Antarctic Peninsula are computed using VADER filters and compared to previous estimates from both, GRACE and other mass balance estimation techniques. The mass change results obtained show very good agreement with other estimates and are robust against variations of the filter strength. The DDK decorrelation filter serves as main benchmark for the assessment of the VADER filter. For comparable filter strengths the VADER filters achieve a better de-striping and deliver smaller formal errors than static filters like the DDK.

Highlights

After its mission ended in 2017, more than a decade of Gravity Recovery and Climate Experiment (GRACE) (Gravity Recovery and ClimateExperiment; [1]) monthly gravity field solutions are available
This study focuses on post-processing strategies, in the form of decorrelation or regularization filters, computed from signal and error covariance information, of GRACE time variable gravity field solutions
In this study we investigate the feasibility and potential of creating non-stationary decorrelation filters based entirely on publicly available data, which can be applied to any GRACE temporal gravity time series that is complemented by full covariance information

Summary

Introduction

After its mission ended in 2017, more than a decade of GRACE This study focuses on post-processing strategies, in the form of decorrelation or regularization filters, computed from signal and error covariance information, of GRACE time variable gravity field solutions. In this study we investigate the feasibility and potential of creating non-stationary decorrelation filters based entirely on publicly available data, which can be applied to any GRACE temporal gravity time series that is complemented by full covariance information. The increasing number of processing centers distributing their error covariance matrices allows for applying decorrelation filters like the VADER filter to a growing selection of GRACE time series and to better exploit the signal content of the GRACE data. The last section provides the main conclusions and a short outlook

Decorrelation Strategy and Setup of Experiments

The Closed-Loop Simulation Environment

Determination of a Favorable Filter Design

Application to Real Data

Estimated

Conclusions