2025/11/18 by K C Sanjib, C, Sanjib K, Viacheslav M. Sadykov +3
Environmental Science · Medicine · Physics and Astronomy · #Atmospheric aerosols and clouds #Atmospheric and Oceanic Physics (physics.ao-ph) #FOS: Physical sciences #Ionosphere and magnetosphere dynamics #Radiation Therapy and Dosimetry #Space Physics (physics.space-ph)
paper · pdf · doi:10.48550/arxiv.2511.15746
openalex publication_date 2025/11/18 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28
Radiation exposure at aviation altitudes presents significant health risks to aircrews due to the cumulative effects of ionizing radiation. Physics-based models estimate radiation levels based on geophysical and atmospheric parameters, but often struggle to capture the highly dynamic and complex nature of the radiation environment, limiting their real-time predictive capabilities. To address this challenge, we investigate machine learning (ML) methods to enhance real-time radiation nowcasting. Leveraging newly compiled ML-ready datasets, publicly available at https://dmlab.cs.gsu.edu/rdp/, we train supervised models capable of capturing both linear and non-linear relationships between Geospace conditions and atmospheric radiation levels. Our experiments demonstrate that the XGBoost model achieves approximately 10 percent improvement in prediction accuracy over the considered physics-based model. Furthermore, feature importance analysis reveals that certain Geospace properties, specifically solar polar fields, solar wind properties, and neutron monitor data, are impacting the nowcast of the radiation levels at flight altitudes. These findings suggest meaningful physical relationships between the near-Earth space environment and atmospheric radiation, and highlight the potential of ML-based approaches for operational space weather applications.