Bottom–up approach for mitigating extreme events under limited intervention options: a case study with Lorenz 96

Abstract. Prediction and mitigation of extreme weather events are important scientific and societal challenges. Recently, Miyoshi and Sun (2022) proposed a control simulation experiment framework that assesses the controllability of chaotic systems under observational uncertainty, and within this framework, Sun et al. (2023) developed a method to prevent extreme events in the Lorenz 96 model. However, since their method is primarily designed to apply control inputs to all grid variables, the success rate decreases to approximately 60 % when applied to a single site, at least in a specific setting. Herein, we propose an approach that mitigates extreme events through local interventions based on multi-scenario ensemble forecasts. The success rate of our method is markedly higher than that of Sun et al.'s method, reaching 94 % even when applying interventions at one site per step, albeit with a moderate increase in the intervention cost. Furthermore, the success rate increases to 99.4 % during interventions at two sites. Unlike control-theoretic approaches adopting a top–down strategy, which determine inputs by optimizing cost functions, our bottom–up approach mitigates extreme events by effectively utilizing limited intervention options.