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A Machine-Learning-Based Global Thermospheric Density Forecasting Model
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DOI：10.1029/2026SW004968.png)
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Abstract
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Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER- P 3 ${\mathrm{P}}^{3}$ (Accelerometer-driven Estimation of THERmospheric density–A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER- P 3 ${\mathrm{P}}^{3}$ formulates thermospheric density forecasting as a sequence-to-sequence regression task conditioned on recent space weather evolution and a user-specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER- P 3 ${\mathrm{P}}^{3}$ incorporates JB2008 and NRLMSISE-00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar-wind drivers. The network employs dual recurrent encoders and an evidential Normal-Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER- P 3 ${\mathrm{P}}^{3}$ achieves high forecast skill ( R > 0.95 ) $(R > 0.95)$ . Under moderate activity, strong skill is retained ( R ≈ 0.93 ) $(R\approx 0.93)$ , with reduced physical-domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust ( R = 0.89 $R=0.89$ –0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER- P 3 ${\mathrm{P}}^{3}$ as a practical, low-latency, uncertainty-aware capability for thermospheric density forecasting that supports orbit prediction, drag-risk assessment, and operational decision-making over its validated altitude range of approximately 300–520 km, with highest confidence in the data-rich 400–520 km region.
Keywords:
machine learning
thermospheric density
density forecasting
uncertainty estimation
space weather
physics-informed framework
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