CHAMP Gravity Field Recovery Using Kinematic Orbits

2005 ◽  
Vol 48 (2) ◽  
pp. 319-325 ◽  
Author(s):  
Tian-He XU ◽  
Yuan-Xi YANG
Keyword(s):  
Gravity Field ◽  
Gravity Field Recovery

scholarly journals Earth’s Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits

2021 ◽  
Vol 13 (9) ◽  
pp. 1766
Author(s):  
Igor Koch ◽  
Mathias Duwe ◽  
Jakob Flury ◽  
Akbar Shabanloui
Keyword(s):  
Gravity Field ◽  
Time Variable ◽  
Time Variable Gravity ◽  
Gravity Field Recovery ◽  
Variable Gravity ◽  
Earth’S System ◽  
Grace Mission ◽  
Insight Into ◽  
K Band

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.

scholarly journals GRACETOOLS—GRACE Gravity Field Recovery Tools

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 350 ◽  
Author(s):  
Neda Darbeheshti ◽  
Florian Wöske ◽  
Matthias Weigelt ◽  
Christopher Mccullough ◽  
Hu Wu
Keyword(s):  
Open Source ◽  
Gravity Field ◽  
Transition Matrix ◽  
State Transition ◽  
Satellite Observations ◽  
Gravity Field Recovery ◽  
Grace Data ◽  
Range Rate ◽  
Analysis Platform ◽  
Recovery Tools

This paper introduces GRACETOOLS, the first open source gravity field recovery tool using GRACE type satellite observations. Our aim is to initiate an open source GRACE data analysis platform, where the existing algorithms and codes for working with GRACE data are shared and improved. We describe the first release of GRACETOOLS that includes solving variational equations for gravity field recovery using GRACE range rate observations. All mathematical models are presented in a matrix format, with emphasis on state transition matrix, followed by details of the batch least squares algorithm. At the end, we demonstrate how GRACETOOLS works with simulated GRACE type observations. The first release of GRACETOOLS consist of all MATLAB M-files and is publicly available at Supplementary Materials.

GRACE Follow-On Gravity Field Recovery from Combined Laser Ranging Interferometer and Microwave Ranging System Measurements

2021 ◽  
Author(s):  
Saniya Behzadpour ◽  
Andreas Kvas ◽  
Torsten Mayer-Gürr
Keyword(s):  
Gravity Field ◽  
Laser Ranging ◽  
Measurement Technology ◽  
Additional Measurement ◽  
Covariance Functions ◽  
Gravity Field Recovery ◽  
Global Gravity ◽  
Least Squares Adjustment ◽  
Noise Covariance ◽  
Component Estimation

<p>Besides a K-Band Ranging System (KBR), GRACE-FO carries a Laser Ranging Interferometer (LRI) as a technology demonstration to provide measurements of inter-satellite range changes. This additional measurement technology provides supplementary observations, which allow for cross-instrument diagnostics with the KBR system and, to some extent, the separation of ranging noise from other sources such as noise in the on-board accelerometer (ACC) measurements.</p><p>The aim of this study is to incorporate the two ranging systems (LRI and KBR) observations in ITSG-Grace2018 gravity field recovery. The two observation groups are combined in an iterative least-squares adjustment with variance component estimation used to determine the unknown noise covariance functions for KBR, LRI, and ACC measurements. We further compare the gravity field solutions obtained from the combined solutions to KBR-only results and discuss the differences with a focus on the global gravity field and LRI calibration parameters.</p>

The Impact of Temporal Gravity Variations on GOCE Gravity Field Recovery

2006 ◽  
pp. 255-269 ◽  
Author(s):  
Oleg Abrikosov ◽  
Focke Jarecki ◽  
Jürgen Müller ◽  
Svetozar Petrovic ◽  
Peter Schwintzer
Keyword(s):  
Gravity Field ◽  
Gravity Field Recovery ◽  
Gravity Variations ◽  
The Impact ◽  
Temporal Gravity

scholarly journals Gravity field recovery from orbit information using the Lagrange formalism

2017 ◽  
Vol 60 (3) ◽  
Author(s):  
Mohammad Ali Sharifi ◽  
Mohammad Reza Seif ◽  
Oliver Baur ◽  
Nico Sneeuw
Keyword(s):  
Gravity Field ◽  
Gravity Field Recovery ◽  
Lagrange Formalism

Gravity Field Recovery and Observation Noise Separation from Simultaneous Laser Ranging Interferometer and K-Band Ranging System Measurements

2020 ◽  
Author(s):  
Andreas Kvas ◽  
Saniya Behzadpour ◽  
Torsten Mayer-Guerr
Keyword(s):  
Gravity Field ◽  
Spherical Harmonic ◽  
Uncertainty Propagation ◽  
Stochastic Modelling ◽  
Laser Ranging ◽  
Polar Regions ◽  
Data Types ◽  
Covariance Functions ◽  
Gravity Field Recovery ◽  
K Band

<p>The unique instrumentation of GRACE Follow-On (GRACE-FO) offers two independent inter-satellite ranging systems with concurrent observations. Next to a K-Band Ranging System (KBR), which has already been proved during the highly-successfully GRACE mission, the GRACE-FO satellites are equipped with an experimental Laser Ranging Interferometer (LRI), which features a drastically increased measurement precision compared to the KBR. Having two simultaneous ranging observations available allows for cross-calibration between the instruments and, to some degree, the separation of ranging noise from other sources such as noise in the on-board accelerometer (ACC) measurements.  </p> <p>In this contribution we present a stochastic description of the two ranging observation types provided by GRACE-FO, which also takes cross-correlations between the two observables into account. We determine the unknown noise covariance functions through variance component estimation and show that this method is, to some extent, capable of separating between KBR, LRI, and ACC noise. A side effect of this stochastic modelling is that the formal errors of the spherical harmonic coefficients fit very well to empirical estimates, which is key for combination with other data types and uncertainty propagation. We further compare the gravity field solutions obtained from a combined least-squares adjustment to KBR-only and LRI-only results and discuss the differences between the time series with a focus on gravity field and calibration parameters. Even though, at the moment, global statistics only show a minor improvement when using LRI ranging measurements instead of KBR observations, some parts of the spectrum and geographic regions benefit significantly from the increased measurement accuracy of the LRI. Specifically, we see a higher signal-to-noise ratio in low spherical harmonic orders and the polar regions.</p>

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