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Record Nr. |
UNINA9910866576303321 |
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Autore |
Goyal Manish Kumar |
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Titolo |
Understanding Atmospheric Rivers Using Machine Learning / / by Manish Kumar Goyal, Shivam Singh |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (78 pages) |
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Collana |
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SpringerBriefs in Applied Sciences and Technology, , 2191-5318 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Chemical engineering |
Environmental engineering |
Atmospheric science |
Machine learning |
Climatology |
Environmental Process Engineering |
Atmospheric Science |
Machine Learning |
Climate Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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