Novel considerations about the error budget of the LAGEOS-based tests of frame-dragging with GRACE geopotential models
Lorenzo Iorio, Matteo Luca Ruggiero, Christian Corda

TL;DR
This paper critically assesses the uncertainties in geopotential models used for LAGEOS-based frame-dragging tests, highlighting potential biases and the importance of independent validation for accurate error estimation.
Contribution
It introduces a novel approach to evaluate the uncertainties of even zonal harmonics, revealing larger error margins and questioning the reliability of previous GRACE-based models in testing general relativity.
Findings
Uncertainties in L=4 and L=6 even zonals are 2-3 and 3-4 10^-11 respectively.
Total gravitational error in LAGEOS tests is approximately 27-28%, with an upper bound of 37-39%.
GRACE SST data alone does not fully constrain low-degree even zonals due to their long-period effects.
Abstract
A realistic assessment of the uncertainties in the even zonals of a given geopotential model must be made by directly comparing its coefficients with those of a wholly independent solution of superior formal accuracy. Otherwise, a favorable selective bias is introduced in the evaluation of the total error budget of the LAGEOS-based Lense-Thirring tests yielding likely too optimistic figures for it. By applying a novel approach which recently appeared in the literature, the second (L = 4) and the third (L = 6) even zonals turn out to be uncertain at a 2-3 10^-11 (L = 4) and 3-4 10^-11 (L = 6) level, respectively, yielding a total gravitational error of about 27-28%, with an upper bound of 37-39%. The results by Ries et al. themselves yield an upper bound for it of about 33%. The low-degree even zonals are not exclusively determined from the GRACE Satellite-to-Satellite Tracking (SST)…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
