A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Journal Article · Thu Nov 13 00:00:00 EST 2025 · Earth and Space Science

DOI:https://doi.org/10.1029/2025EA004633· OSTI ID:3007516

Noh, Sung Jun ^[1]; Morley, Steven Karl ^[1]; Cowee, Misa M. ^[1]; Jordanova, Vania Koleva ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 3007516
• Report Number(s): LA-UR--25-28043; 10.1029/2025EA004633; 2333-5084
• Journal Information: Earth and Space Science, Journal Name: Earth and Space Science Journal Issue: 11 Vol. 12; ISSN 2333-5084
• Publisher: American Geophysical Union (AGU) - Wiley
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS