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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13091766