VeilSight v9 – Weather Regime Intelligence: Environmental Forcing with Live NOAA Data

VeilSight v9 – Weather Regime Intelligence: Environmental Forcing with Live NOAA Data This release (v9) extends the VeilSight framework to incorporate real-time environmental forcing through live NOAA weather data, demonstrating how external stressors (temperature, wind, precipitation, pressure) warp regime space and drive states toward irreversible commitments. Building directly on prior versions: v2–v3: Empirical discovery and phase-structured motion glyphs as attractor states v4: Symbolic neuro-symbolic prototyping v5: RegimeGuard with Omega Glyph as irreversibility anchor v6: Integrity-based filtering across equities, crypto, sports v7: Energy-field model and regime confidence v8: Cross-linguistic invariance in ancient texts (Isaiah 53) Key Innovations in v9 Integration of live NOAA GHCNd station data (e.g., USW00094728 – New York City Central Park extremes) as dynamic forcing inputs Real-time computation of weather-derived stress (wind, temp deviation, precipitation) as a multiplier on persistence, entropy, and Omega distance Interactive 3D phase portrait visualization showing basin drift under forcing (persistence × entropy × stability decay, colored by Omega distance) Drift animation (10-second snippet) demonstrating temporal progression toward or away from the Omega Glyph attractor (fixed at UGI ≈ 0.1667) Deterministic regime classification (FORCED vs. transitional/stable) with audit-ready ledger export (JSON/PDF) What-if simulator for environmental stress (sliders for wind/temp) to explore hypothetical irreversibility Data Sources & Verification Raw hourly observations from NOAA GHCNd stations (temperature, wind speed, pressure) Derived metrics: Weather entropy, drift, persistence, stability decay, total stress, adjusted Omega distance All computations deterministic and reproducible from provided CSVs and scripts Files Included weather_states.csv – Processed regime metrics with timestamps and classifications raw_weather.csv – Raw NOAA inputs (temperature, wind, pressure) Screenshots & animation MP4/GIF – 3D basin view, drift animation, regime status Streamlit dashboard code snippet (for replication) README.md – Setup instructions, how to run locally, interpretation guide Implications v9 proves the framework's domain-independence: the same structural invariants (persistence dominance, entropy collapse, Omega proximity) hold under extreme environmental forcing as they do in motion video, markets, engines, and historical texts. This enables applications in infrastructure risk, climate adaptation, edge AI, and safety-critical systems. Previous Versions v8: https://doi.org/10.5281/zenodo.18207655 v7: https://doi.org/10.5281/zenodo.18197515 v6: https://doi.org/10.5281/zenodo.18188697 v5: https://doi.org/10.5281/zenodo.18175350 v4: https://doi.org/10.5281/zenodo.18139710 v3: https://doi.org/10.5281/zenodo.18000536 v2: https://doi.org/10.5281/zenodo.17999505 Foundational: https://doi.org/10.5281/zenodo.17992406 License: CC-BY 4.0 Keywords: regime detection, environmental forcing, Omega Glyph, irreversibility, NOAA weather, dynamical systems, neuro-symbolic AI, structural intelligence Created by: Damon Rice, Ormond Beach, FL