Machine Learning and Data Mining Approaches to Climate Science

Proceedings of the 4th International Workshop on Climate Informatics

Editors: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (Eds.)

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  • State of the art application in a new and rapidly expanding field
  • Includes review articles by acknowledged experts
  • Presents novel research in climate informatics
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  • ISBN 978-3-319-17220-0
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  • Due: November 4, 2016
  • ISBN 978-3-319-36558-9
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About this book

This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014.

Table of contents (22 chapters)

Table of contents (22 chapters)
  • Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System

    Pages 3-12

    Tandeo, Pierre (et al.)

  • Machine Learning Methods for ENSO Analysis and Prediction

    Pages 13-21

    Lima, Carlos H. R. (et al.)

  • Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems

    Pages 23-33

    Rheinwalt, Aljoscha (et al.)

  • Comparison of Linear and Tobit Modeling of Downscaled Daily Precipitation over the Missouri River Basin Using MIROC5

    Pages 35-49

    Popuri, Sai K. (et al.)

  • Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data

    Pages 51-58

    Chen, Xi C. (et al.)

Buy this book

eBook $169.00
price for USA in USD
  • ISBN 978-3-319-17220-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.99
price for USA in USD
  • ISBN 978-3-319-17219-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover $219.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: November 4, 2016
  • ISBN 978-3-319-36558-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning and Data Mining Approaches to Climate Science
Book Subtitle
Proceedings of the 4th International Workshop on Climate Informatics
Editors
  • Valliappa Lakshmanan
  • Eric Gilleland
  • Amy McGovern
  • Martin Tingley
Copyright
2015
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-17220-0
DOI
10.1007/978-3-319-17220-0
Hardcover ISBN
978-3-319-17219-4
Softcover ISBN
978-3-319-36558-9
Edition Number
1
Number of Pages
IX, 252
Number of Illustrations
16 b/w illustrations, 73 illustrations in colour
Topics