Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather

TLDR

High‑impact weather events such as severe thunderstorms, tornadoes, and hurricanes cause major infrastructure damage, property loss, and fatalities, while also offering benefits like savings through renewable energy, and their prediction has improved with better observations, computing, and physics, yet still requires further gains. The study demonstrates that applying AI techniques with physical understanding can significantly improve prediction skill for multiple high‑impact weather types. The authors employ machine‑learning and data‑mining methods to fuse forecast model output with observations, enhancing accuracy and providing real‑time decision support for predicting storm duration, severe wind, hail, precipitation classification, renewable‑energy forecasting, and aviation turbulence. Results indicate that AI combined with physical knowledge markedly improves prediction skill, contributes to computational sustainability, and enables big‑data processing that yields deeper insights into high‑impact weather phenomena.

Abstract

Abstract High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.