04098nam a22006255i 4500 978-3-031-63478-9 DE-He213 20251030112327.0 cr nn 008mamaa 240622s2024 sz | s |||| 0|eng d 9783031634789 978-3-031-63478-9 10.1007/978-3-031-63478-9 doi TP155-156 TA170-171 TDC bicssc TQ bicssc TEC009010 bisacsh TDCX thema TQ thema Goyal, Manish Kumar. author. (orcid)0000-0001-9777-6128 https://orcid.org/0000-0001-9777-6128 aut http://id.loc.gov/vocabulary/relators/aut 79166 Understanding Atmospheric Rivers Using Machine Learning [electronic resource] / by Manish Kumar Goyal, Shivam Singh. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 74 p. 30 illus., 29 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Applied Sciences and Technology, 2191-5318 Understanding Atmospheric Rivers and Exploring Their Role as Climate Extremes -- Characterization and Impacts of Atmospheric Riversharacterization and Impacts of Atmospheric Rivers -- Key Characteristics of Atmospheric Rivers and Associated Precipitation -- Major Large-Scale Climate Oscillations and their Interactions with Atmospheric Rivers -- Role of Machine Learning in Understanding and Managing Atmospheric Rivers. 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 manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (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 research and the synergy between atmospheric science, climatology, and artificial intelligence. Chemical engineering. Environmental engineering. Atmospheric science. Machine learning. 55059 Climatology. 62309 Environmental Process Engineering. 68161 Atmospheric Science. Machine Learning. 55059 Climate Sciences. 67755 Singh, Shivam. author. (orcid)0000-0002-2367-0256 https://orcid.org/0000-0002-2367-0256 aut http://id.loc.gov/vocabulary/relators/aut 79167 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031634772 Printed edition: 9783031634796 SpringerBriefs in Applied Sciences and Technology, 2191-5318 65846 https://doi.org/10.1007/978-3-031-63478-9 ZDB-2-ENG ZDB-2-SXE 60945 60945