The atmosphere contains structured, semi-predictable kinetic energy across scales from planetary circulation to microscale turbulence — yet no integrated computational framework has existed to characterize it as a unified physical quantity. We present AEROTICA, an Atmospheric Kinetic Efficiency (AKE) index combining nine physically orthogonal parameters — Kinetic Energy Density (KED), Turbulence Intensity Index (TII), Vertical Shear Ratio (VSR), Aerosol Optical Depth (AOD), Thermal Helicity Dynamics (THD), Pressure Gradient Force (PGF), Humidity–Convection Interaction (HCI), Atmospheric Stability Integration (ASI), and Local Roughness Coefficient (LRC) — into a single Bayesian-weighted composite index implemented via Physics-Informed Neural Networks (PINNs) enforcing Navier-Stokes consistency. Validated across 3, 412 station-years from 24 national networks spanning 35 countries and 6 climate zones, AEROTICA achieves 96. 2% AKE classification accuracy against Large Eddy Simulation benchmarks, ±28-second gust timing precision across 1, 247 severe wind events, and 97. 1% offshore accuracy. Prospective deployment in Casablanca (214 days, 47 stations) demonstrates a gust pre-alert probability of detection of 0. 886 with 4–6 minute lead time and estimated annual economic benefit of €124M (287× ROI). Building-integrated assessments in Brest and Edinburgh identify 180 GWh/year of harvestable urban wind previously invisible to legacy atlases. PINN inference completes in under 90 seconds per 50×50 km domain. AEROTICA addresses the estimated 47 billion annual global cost of inadequate atmospheric kinetic energy characterization.
Samir Baladi (Thu,) studied this question.