東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Understanding atmospheric rivers using machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding atmospheric rivers using machine learning/ by Manish Kumar Goyal, Shivam Singh.
作者:	Goyal, Manish Kumar.
其他作者:	Singh, Shivam.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	viii, 74 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Atmospheric waves. -
電子資源:	https://doi.org/10.1007/978-3-031-63478-9
ISBN:	9783031634789

Understanding atmospheric rivers using machine learning
Goyal, Manish Kumar.

Understanding atmospheric rivers using machine learning[electronic resource] /by Manish Kumar Goyal, Shivam Singh. - Cham :Springer Nature Switzerland :2024. - viii, 74 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-5318. - SpringerBriefs in applied sciences and technology..

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.

ISBN: 9783031634789

Standard No.: 10.1007/978-3-031-63478-9doiSubjects--Topical Terms:

603233
Atmospheric waves.

LC Class. No.: QC880.4.W3

Dewey Class. No.: 551.52

Understanding atmospheric rivers using machine learning
LDR:02837nmm a2200361 a 4500 001 2372401
003 DE-He213
005 20240622125251.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031634789 $q (electronic bk.)
020 $a 9783031634772 $q (paper)
024 7 $a 10.1007/978-3-031-63478-9 $2 doi
035 $a 978-3-031-63478-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QC880.4.W3
072 7 $a TDC $2 bicssc
072 7 $a TQ $2 bicssc
072 7 $a TEC009010 $2 bisacsh
072 7 $a TDCX $2 thema
072 7 $a TQ $2 thema
082 0 4 $a 551.52 $2 23
090 $a QC880.4.W3 $b G724 2024
100 1 $a Goyal, Manish Kumar. $3 3526477
245 1 0 $a Understanding atmospheric rivers using machine learning $h [electronic resource] / $c by Manish Kumar Goyal, Shivam Singh.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a viii, 74 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-5318
505 0 $a 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.
520 $a 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.
650 0 $a Atmospheric waves. $3 603233
650 0 $a Machine learning. $3 533906
650 1 4 $a Environmental Process Engineering. $3 3631514
650 2 4 $a Atmospheric Science. $3 3592263
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Climate Sciences. $3 3592220
700 1 $a Singh, Shivam. $3 3719127
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied sciences and technology. $3 1565541
856 4 0 $u https://doi.org/10.1007/978-3-031-63478-9
950 $a Engineering (SpringerNature-11647)