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
| ISBN: | 9783031634772 |
| Publication date: | 23rd June 2024 |
| Author: | Manish Kumar Goyal, Shivam Singh |
| Publisher: | Springer an imprint of Springer Nature Switzerland |
| Format: | Paperback |
| Pagination: | 74 pages |
| Series: | SpringerBriefs in Applied Sciences and Technology |
| Genres: |
Process engineering technology and techniques Machine learning Meteorology and climatology Environmental science, engineering and technology |
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
Understanding Atmospheric Rivers Using Machine Learning features in the following genres: Process engineering technology and techniques, Machine learning, Meteorology and climatology, Environmental science, engineering and technology
Understanding Atmospheric Rivers Using Machine Learning is available in Paperback
Understanding Atmospheric Rivers Using Machine Learning was written by Manish Kumar Goyal, Shivam Singh and published by Springer an imprint of Springer Nature Switzerland
Understanding Atmospheric Rivers Using Machine Learning has 74 pages
Yes it is part of SpringerBriefs in Applied Sciences and Technology series
£40.49