Table of contents

  1. Front Matter
    Pages i-ix
  2. Machine Learning Methods

    1. Front Matter
      Pages 1-1
    2. Pierre Tandeo, Pierre Ailliot, Juan Ruiz, Alexis Hannart, Bertrand Chapron, Anne Cuzol et al.
      Pages 3-12
    3. Carlos H. R. Lima, Upmanu Lall, Tony Jebara, Anthony G. Barnston
      Pages 13-21
    4. Aljoscha Rheinwalt, Bedartha Goswami, Niklas Boers, Jobst Heitzig, Norbert Marwan, R. Krishnan et al.
      Pages 23-33
    5. Xi C. Chen, Ankush Khandelwal, Sichao Shi, James H. Faghmous, Shyam Boriah, Vipin Kumar
      Pages 51-58
  3. Statistical Methods

    1. Front Matter
      Pages 59-59
    2. Arthur M. Greene, Tracy Holsclaw, Andrew W. Robertson, Padhraic Smyth
      Pages 61-69
    3. Valliappa Lakshmanan, Darrel Kingfield
      Pages 71-79
    4. Megan Heyman, Snigdhansu Chatterjee
      Pages 81-90
    5. Seth McGinnis, Doug Nychka, Linda O. Mearns
      Pages 91-99
    6. Saurabh Agrawal, Trent Rehberger, Stefan Liess, Gowtham Atluri, Vipin Kumar
      Pages 101-109
  4. Discovery of Climate Processes

    1. Front Matter
      Pages 111-111
    2. Brandon A. Mayer, Brian McKenna, Alexander Crosby, Kelly Knee
      Pages 127-135
  5. Analysis of Climate Records

    1. Front Matter
      Pages 161-161
    2. Niklas Boers, Aljoscha Rheinwalt, Bodo Bookhagen, Norbert Marwan, Jürgen Kurths
      Pages 163-174

About these proceedings

Introduction

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.

Keywords

Climate Extremes Climate Informatics Climate Prediction Data Mining Pattern Recognition for Climate

Editors and affiliations

  • Valliappa Lakshmanan
    • 1
  • Eric Gilleland
    • 2
  • Amy McGovern
    • 3
  • Martin Tingley
    • 4
  1. 1.The Climate CorporationSeattleUSA
  2. 2.Research Applications LaboratoryNational Center for Atmospheric ResearchBoulderUSA
  3. 3.Computer ScienceUniversity of OklahomaNormanUSA
  4. 4.Meteorology and StatisticsPennsylvania State UniversityUniversity ParkUSA

Bibliographic information

  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-319-17219-4
  • Online ISBN 978-3-319-17220-0