Mon Aug 24, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Dietrich, Nicholas and Matsuo, Tomoko, 2026. Quantifying Storm–Time Neutral Density Uncertainties using a Physics–Based Particle Filter Framework. Journal of the Astronautical Sciences, 73(3):49, doi:10.1007/s40295-026-00595-x.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026JAnSc..73...49D,
author = {{Dietrich}, Nicholas and {Matsuo}, Tomoko},
title = "{Quantifying Storm-Time Neutral Density Uncertainties using a Physics-Based Particle Filter Framework}",
journal = {Journal of the Astronautical Sciences},
keywords = {Neutral mass density, Atmospheric drag, Particle filter, Uncertainty quantification, Data assimilation},
year = 2026,
month = jun,
volume = {73},
number = {3},
eid = {49},
pages = {49},
abstract = "{Forecasting orbit positions in the low Earth orbit (LEO) environment is
necessary for avoiding collisions, which is particularly
difficult during geomagnetic storms due to highly variable
neutral mass density and the associated uncertainty on
atmospheric drag. Orbit position errors are commonly assumed to
follow a Gaussian distribution, being represented with a
covariance ellipsoid, but nonlinear neutral density variability
during storms is likely to disrupt this assumption. This study
attempts to quantify storm-time non-Gaussian neutral density
uncertainties by using a new physics-based particle filter
framework. The framework is implemented to globally estimate the
density field and a small state-space of forcing parameters,
while utilizing a first-principles physics-based model of the
ionosphere-thermosphere (I-T) system. The National Center for
Atmospheric Research Thermosphere Ionosphere Electrodynamics
General Circulation Model (TIEGCM) is used as the physics-based
model. The framework is applied to an isolated storm event in
July 2022, using neutral density observations retrieved from the
Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)
mission's onboard accelerometer. The outcome is a time-varying
characterization of uncertainty in non-Gaussian forcing
parameters and neutral density fields arising from nonlinear
storm-time I-T dynamics. Filter results show bimodal
distributions for the day and night sides and reveal how regions
with high non-Gaussian forcing parameter distributions vary
through the storm period. The development of this physics-based
particle filter framework represents the first steps towards
future efforts in quantifying the impact of nonlinear LEO
neutral density dynamics on orbit position errors.}",
doi = {10.1007/s40295-026-00595-x},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026JAnSc..73...49D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Aug 24, F. Flechtner