# GRACE-FO Satellite Data Preprocessing Based on Residual Iterative Correction and Its Application to Gravity Field Inversion

## TL;DR

This paper introduces a new data preprocessing method for GRACE-FO satellite data that improves gravity field inversion accuracy significantly.

## Contribution

A novel residual iterative correction framework is proposed to enhance satellite gravimetry data quality and inversion precision.

## Key findings

- The method effectively reduces data discontinuities and improves data quality in satellite gravimetry.
- Gravity field model accuracy improved by two orders of magnitude, reaching 10−8–10−9.
- The model was validated against established models from CSR, GFZ, and JPL.

## Abstract

To address the limited inversion accuracy caused by low-fidelity data in satellite gravimetry, this study proposes a data preprocessing framework based on iterative residual correction. Utilizing Level-1B observations from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite (January 2020), outliers were systematically detected and removed, while data gaps were compensated through spline interpolation. Experimental results demonstrate that the proposed method effectively mitigates data discontinuities and anomalous perturbations, achieving a significant improvement in data quality. Furthermore, a 60-order Earth gravity field model derived via the energy balance approach was validated against contemporaneous models published by the University of Texas Center for Space Research (CSR), German Research Centre for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL). The results reveal a two-order-of-magnitude enhancement in inversion precision, with model accuracy improving from 10−6–10−7 to 10−8–10−9. This method provides a robust solution for enhancing the reliability of gravity field recovery in satellite-based geodetic missions.