Titlebook: Understanding Atmospheric Rivers Using Machine Learning; Manish Kumar Goyal,Shivam Singh Book 2024 The Author(s), under exclusive license

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2191-530X relevance.This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestat

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Characterization and Impacts of Atmospheric Rivers,outh America, and Polar Regions. The relationship between ARs and LSCOs (ENSO, MJO, PDO, etc.) can provide valuable insights into the predictability and variability of AR events. The impacts of ARs are multifaceted, encompassing both beneficial and detrimental effects, such as flooding, drought, and

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Major Large-Scale Climate Oscillations and Their Interactions with Atmospheric Rivers, variations capturing these variations more effectively during certain time scales. These findings have important implications for climate forecasting, water resource management, and adaptation strategies. By understanding and leveraging the connections between LSCOs, ARs, and precipitation extremes

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Role of Machine Learning in Understanding and Managing Atmospheric Rivers,olutional 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 understandi

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Book 2024ntelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR resea

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pproximate solutions. Stabilizing properties such as smoothness and shape constraints imposed on the solution are used. On the basis of these investigations, we propose and establish efficient regularization algorithms for stable numerical solution of a wide class of ill-posed problems. In particular, descrip978-90-481-5382-4978-94-015-9482-0