INFRAS-CLOUD demonstrates 93.1% accuracy classifying severe weather events like tropical cyclones using infrasonic signals.
INFRAS-CLOUD presents a physics-informed eight-parameter Atmospheric Infrasonic Severity Index (AISI) for real-time classification of severe weather, volcanic, and ocean-atmosphere infrasonic events. Eight governing acoustic parameters — spectral peak frequency (f_p), microbarom amplitude (P_ub), azimuthal arrival angle (θ), stratospheric ducting efficiency (D_str), phase velocity (v_ph), inter-station coherence (γ²), atmospheric absorption coefficient (α_air), and signal-to-noise ratio (SNR) — are integrated via PCA-regularized logistic regression into a single composite severity score (AISI). Validated across 1,847 events from 47 International Monitoring System (IMS) stations spanning 2005–2025, INFRAS-CLOUD achieves 93.1% classification accuracy across six source categories (tropical cyclones, tornadoes, volcanic explosions, microbaroms, mountain-associated waves, anthropogenic). Key results include: tornado precursor lead times of 12–28 minutes prior to NWS confirmation; tropical cyclone detection at ranges up to 4,200 km; volcanic energy estimation within 10% of independent seismic benchmarks (Hunga Tonga 2022: 38 ± 4 MT TNT); and stratospheric wind inversion accurate to ±7.8 m/s without radiosonde requirements. The AISI framework is released as an open-source Python package (pip install infrascloud) under MIT License.
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Samir Baladi (2026) studied this question.
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