Role of Machine Learning in Understanding and Managing Atmospheric Rivers

ARs are observed highly correlated with floods in almost all major continents. Robust and accurate forecasting of landfalling ARs at a substantial lead time can help in mitigating harmful impacts of these ARs. ARs, characterized by their long and narrow corridors of concentrated moisture transport, present challenges in accurate prediction and understanding due to their intricate spatiotemporal features. Traditional Numerical Weather Prediction (NWP) models, while foundational, encounter limitations in precisely capturing AR behaviors, especially over extended lead times. Motivated by the capability of Artificial Intelligence (AI) to handle complex datasets and discern intricate patterns, this study delves into exploring possible applications of AI to model AR characteristics without explicitly encoding physical processes. By leveraging AI techniques such as deep neural networks and convolutional architectures, this chapter aims to present AI as a tool to improve the prediction, classification, and tracking of ARs. This paper reviews the potential and challenges associated with AI applications in AR analysis and management, highlighting its pivotal role in enhancing our understanding and preparedness in dealing with these significant meteorological events.