Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Pan, Qian, Xiong, Chao, Gao, ShunZu, Chen, Zhou, Smirnov, Artem, Xu, Chunyu, and Huang, Yuyang, 2025. Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE-FO Satellite Data. Space Weather, 23(3):2024SW004259, doi:10.1029/2024SW004259.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025SpWea..2304259P,
author = {{Pan}, Qian and {Xiong}, Chao and {Gao}, ShunZu and {Chen}, Zhou and {Smirnov}, Artem and {Xu}, Chunyu and {Huang}, Yuyang},
title = "{Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE-FO Satellite Data}",
journal = {Space Weather},
year = 2025,
month = mar,
volume = {23},
number = {3},
pages = {2024SW004259},
abstract = "{With rapid development of artificial intelligence technology, machine
learning has been widely applied to the thermospheric mass
density (TMD) modeling. In this study we propose a machine-
learning approach, the bidirectional gated recurrent unit with
multi-head attention mechanism (BGMA), for modeling and
predicting the TMD, based on the Gravity Recovery and Climate
Experiment (GRACE) satellite data. GRACE data spanning over one
solar cycle provide a valuable opportunity to explore altitude
and solar activity dependencies in TMD. We selected 11 key
parameters to construct the model, the correlation coefficient
(R$^{2}$) between the model's predictions and satellite
observations is 0.944, significantly outperforming the 0.893 of
the latest version of the Naval Research Laboratory Mass
Spectrometer and Incoherent Scatter Radar Extended Model
(NRLMSIS-2.0). In addition, the TMD measurements from GRACE
Follow-On satellite served as an independent test to evaluate
the BGMA model's generalization, yielding an R$^{2}$ of 0.924,
underscoring the model's robustness. A critical aspect of our
work is minimizing the number of input parameters while
maintaining high prediction accuracy. And the interpretability
analysis of the input parameters using the Shapley additive
explanation algorithm has been applied, revealing that the
altitude, solar activity index P10.7 and solar zenith angle are
the three most influential parameters affecting TMD variations
at GRACE satellite. When using just these three parameters, the
R$^{2}$ still reaches 0.842. Additionally, our model
demonstrated robust performance over varying prediction
durations, with R$^{2}$ exceeding 0.800 for predictions
extending up to 1 hr. These results highlight the BGMA model's
effectiveness in accurately predicting TMD.}",
doi = {10.1029/2024SW004259},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025SpWea..2304259P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner