Bhargav Joshi, Digantara; Saikat Majumder, Digantara; Thamim Ansari, Digantara; Tanveer Ahmed, Digantara
Keywords: SSA, Space Weather, Generative AI, Diffusion Model
Abstract:
Thermospheric mass density variations driven by space weather significantly impact satellite drag in Low-Earth Orbit (LEO). This was starkly demonstrated in February 2022 when enhanced atmospheric drag during minor geomagnetic storms led to the loss of 38 Starlink satellites. With the increasing number of LEO satellites and the approaching peak of Solar Cycle 25, satellite operations face heightened risks from extreme space weather events, such as the G5-level May 2024 Gannon storm. Accurate atmospheric density modeling under such conditions is thus crucial for precise orbit prediction and the safety of spaceflight. Over the past few decades, various empirical and physics-based thermospheric density models have been developed and evaluated for drag calculations. However, due to the dynamic nature of space weather and atmospheric variability, no single model has emerged as the definitive operational standard. Empirical models typically exhibit uncertainties of approximately 10–15% under nominal conditions, while physics-based models struggle with inherent assumptions, resulting in uncertainty quantification, a key challenge. Advances in AI offer a promising solution: diffusion models, a class of Generative AI, iteratively refine noisy data to generate realistic representations. Their probabilistic nature allows for inherent uncertainty quantification through multiple samplings of the stochastic denoising process. This paper presents a diffusion-based model for accurate thermospheric density prediction, designed to enhance satellite operators’ decision-making in varying space weather conditions including geomagnetic storms.
Date of Conference: September 16-19, 2025
Track: Atmospherics/Space Weather