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Machine Learning and Data Mining Approaches to Climate Science

Proceedings of the 4th International Workshop on Climate Informatics

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Valliappa Lakshmanan

    1. The Climate Corporation, Seattle, USA

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  2. Eric Gilleland

    1. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, USA

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  3. Amy McGovern

    1. Computer Science, University of Oklahoma, Norman, USA

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  4. Martin Tingley

    1. Meteorology and Statistics, Pennsylvania State University, University Park, USA

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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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Table of contents (22 papers)

  1. Front Matter

    Pages i-ix
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  2. Machine Learning Methods

    1. Front Matter

      Pages 1-1
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    2. Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System

      • Pierre Tandeo, Pierre Ailliot, Juan Ruiz, Alexis Hannart, Bertrand Chapron, Anne Cuzol et al.
      Pages 3-12

    3. Machine Learning Methods for ENSO Analysis and Prediction

      • Carlos H. R. Lima, Upmanu Lall, Tony Jebara, Anthony G. Barnston
      Pages 13-21

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

      • Aljoscha Rheinwalt, Bedartha Goswami, Niklas Boers, Jobst Heitzig, Norbert Marwan, R. Krishnan et al.
      Pages 23-33

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

      • Sai K. Popuri, Nagaraj K. Neerchal, Amita Mehta
      Pages 35-49

    6. Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data

      • 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
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    2. A Bayesian Multivariate Nonhomogeneous Markov Model

      • Arthur M. Greene, Tracy Holsclaw, Andrew W. Robertson, Padhraic Smyth
      Pages 61-69

    3. Extracting the Climatology of Thunderstorms

      • Valliappa Lakshmanan, Darrel Kingfield
      Pages 71-79

    4. Predicting Crop Yield via Partial Linear Model with Bootstrap

      • Megan Heyman, Snigdhansu Chatterjee
      Pages 81-90

    5. A New Distribution Mapping Technique for Climate Model Bias Correction

      • Seth McGinnis, Doug Nychka, Linda O. Mearns
      Pages 91-99

    6. Evaluation of Global Climate Models Based on Global Impacts of ENSO

      • Saurabh Agrawal, Trent Rehberger, Stefan Liess, Gowtham Atluri, Vipin Kumar
      Pages 101-109

  4. Discovery of Climate Processes

    1. Front Matter

      Pages 111-111
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    2. Using Causal Discovery Algorithms to Learn About Our Planet’s Climate

      • Imme Ebert-Uphoff, Yi Deng
      Pages 113-126

    3. SCI-WMS: Python-Based Web Mapping Service for Visualizing Geospatial Data

      • Brandon A. Mayer, Brian McKenna, Alexander Crosby, Kelly Knee
      Pages 127-135

    4. Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard Sampling

      • Vyacheslav Lyubchich, Brian R. Gray, Yulia R. Gel
      Pages 137-145

    5. Kernel and Information-Theoretic Methods for the Extraction and Predictability of Organized Tropical Convection

      • Eniko Székely, Dimitrios Giannakis, Andrew J. Majda
      Pages 147-159

  5. Analysis of Climate Records

    1. Front Matter

      Pages 161-161
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    2. A Complex Network Approach to Investigate the Spatiotemporal Co-variability of Extreme Rainfall

      • Niklas Boers, Aljoscha Rheinwalt, Bodo Bookhagen, Norbert Marwan, Jürgen Kurths
      Pages 163-174
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Keywords

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.

Editors and Affiliations

  • The Climate Corporation, Seattle, USA

    Valliappa Lakshmanan

  • Research Applications Laboratory, National Center for Atmospheric Research, Boulder, USA

    Eric Gilleland

  • Computer Science, University of Oklahoma, Norman, USA

    Amy McGovern

  • Meteorology and Statistics, Pennsylvania State University, University Park, USA

    Martin Tingley

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

  • DOI: https://doi.org/10.1007/978-3-319-17220-0

  • Publisher: Springer Cham

  • eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)

  • Copyright Information: Springer International Publishing Switzerland 2015

  • Hardcover ISBN: 978-3-319-17219-4Published: 10 July 2015

  • Softcover ISBN: 978-3-319-36558-9Published: 15 October 2016

  • eBook ISBN: 978-3-319-17220-0Published: 30 June 2015

  • Edition Number: 1

  • Number of Pages: IX, 252

  • Number of Illustrations: 16 b/w illustrations, 73 illustrations in colour

  • Topics: Atmospheric Sciences, Climatology, Climate Change

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