• Sorted by Date • Sorted by Last Name of First Author •
Lecomte, Hugo, Rosat, Severine, and Mandea, Mioara, 2024. Gap filling between GRACE and GRACE-FO missions: assessment of interpolation techniques. Journal of Geodesy, 98(12):107, doi:10.1007/s00190-024-01917-3.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024JGeod..98..107L,
author = {{Lecomte}, Hugo and {Rosat}, Severine and {Mandea}, Mioara},
title = "{Gap filling between GRACE and GRACE-FO missions: assessment of interpolation techniques}",
journal = {Journal of Geodesy},
keywords = {Variable gravity field, Gap filling, Interpolation, GRACE, Swarm},
year = 2024,
month = dec,
volume = {98},
number = {12},
eid = {107},
pages = {107},
abstract = "{We propose a benchmark for comparing gap-filling techniques used on
global time-variable gravity field time-series. The Gravity
Recovery and Climate Experiment (GRACE) and the GRACE Follow-On
missions provide products to study the Earth's time-variable
gravity field. However, the presence of missing months in the
measurements poses challenges for understanding specific Earth
processes through the gravity field. We reproduce, adapt, and
compare satellite-monitoring and interpolation techniques for
filling these missing months in GRACE and GRACE Follow-On
products on a global scale. Satellite-monitoring techniques
utilize solutions from Swarm and satellite laser ranging, while
interpolation techniques rely on GRACE and/or Swarm solutions.
We assess a wide range of interpolation techniques, including
least-squares fitting, principal component analysis, singular
spectrum analysis, multichannel singular spectrum analysis,
auto-regressive models, and the incorporation of prior data in
these techniques. To inter-compare these techniques, we employ a
remove-and-restore approach, removing existing GRACE products
and predicting missing months using interpolation techniques. We
provide detailed comparisons of the techniques and discuss their
strengths and limitations. The auto-regressive interpolation
technique delivers the best score according to our evaluation
metric. The interpolation based on a least-squares fitting of
constant, trend, annual, and semi-annual cycles offers a simple
and effective prediction with a good score. Through this
assessment, we establish a starting benchmark for gap-filling
techniques in Earth's time-variable gravity field analysis.}",
doi = {10.1007/s00190-024-01917-3},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024JGeod..98..107L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Generated by
bib2html_grace.pl
(written by Patrick Riley
modified for this page by Volker Klemann) on
Mon Oct 13, 2025 16:16:52
GRACE-FO
Mon Oct 13, F. Flechtner![]()