Mon Dec 15, F. Flechtner
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Panpiboon, Patapong, 2025. Tree-Based Analysis of Geophysical and Orbital Influences on NRLMSIS 2.1 Residuals during Geomagnetic Storms. Pure and Applied Geophysics, .
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025PApGe.tmp..258P,
author = {{Panpiboon}, Patapong},
title = "{Tree-Based Analysis of Geophysical and Orbital Influences on NRLMSIS 2.1 Residuals during Geomagnetic Storms}",
journal = {Pure and Applied Geophysics},
keywords = {Thermospheric density, NRLMSIS 2.1, Geomagnetic storms, Solar cycle 25, Random forest regression, Feature importance},
year = 2025,
month = nov,
abstract = "{Thermospheric density models are essential for satellite operations, yet
they exhibit significant discrepancies during geomagnetic
storms. This study evaluates the performance of the Naval
Research Laboratory Mass Spectrometer and Incoherent Scatter
Radar Exosphere (NRLMSIS) 2.1 model during geomagnetic
disturbances in Solar Cycle 25's ascending phase (2021-2024)
using Swarm-A, -B, -C, and GRACE-FO satellite measurements.
Tree-based machine learning algorithms were employed to analyze
model residuals and identify key factors influencing prediction
accuracy. Random Forest Regression provided comparatively better
performance among tested methods (R$^{2}$ = 0.52), explaining
about half of the residual variance. Feature importance analysis
revealed solar flux (F10.7, importance = 0.384), altitude
(0.221), and Dst index (0.127) as the top three factors in this
test. These results suggest that refined empirical formulations
may improve the representation of storm-time density variations.}",
doi = {10.1007/s00024-025-03861-5},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025PApGe.tmp..258P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Dec 15, F. Flechtner