Turning chaotic sensitivity from a bug into a feature: Using physical modeling and deep learning to alter the paths of storms and mitigate extreme weather events

Qin Huang, Moyan Liu, and Upmanu Lall write: Extreme weather events, e.g., droughts, floods, heatwaves, and freezes, are increasing in frequency and intensity, posing severe socio-economic impacts as growing populations heighten exposure to risks that conventional infrastructure cannot fully address. We propose supplementing disaster management with Weather Jiu-Jitsu: a strategy that exploits the chaotic sensitivity of mid-latitude atmospheric dynamics to redirect destructive weather trajectories through small, precisely timed perturbations guided by Finite-Time Lyapunov Exponent (FTLE) diagnostics and deep learning forecast models. They continue: Proof-of-concept experiments using the Aurora deep-learning Earth system model show that FTLE-guided nudges applied days before peak impact can shift a hurricane track to avoid landfall on a major city, weaken the peak intensity of a blocking-driven cold extreme, and reduce atmospheric river moisture transport under favorable upstream…