Machine learning and data mining approaches to climate science [electronic resource] : proceedings of the 4th International Workshop on Climate Informatics / Valliappa Lakshmanan, Eric Gilleland, Amy McGovern, Martin Tingley, editors.
2015
QC981
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Title
Machine learning and data mining approaches to climate science [electronic resource] : proceedings of the 4th International Workshop on Climate Informatics / Valliappa Lakshmanan, Eric Gilleland, Amy McGovern, Martin Tingley, editors.
ISBN
9783319172200 electronic book
3319172204 electronic book
9783319172194
3319172204 electronic book
9783319172194
Published
Cham ; New York : Springer, [2015]
Copyright
©2015
Language
English
Description
1 online resource : illustrations.
Item Number
10.1007/978-3-319-17220-0 doi
Call Number
QC981
Dewey Decimal Classification
551.6
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Added Author
Lakshmanan, Valliappa, editor.
Gilleland, Eric, editor.
McGovern, Amy, editor.
Tingley, Martin, editor.
Gilleland, Eric, editor.
McGovern, Amy, editor.
Tingley, Martin, editor.
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Table of Contents
From the Contents: Machine learning, statistics, or data mining, applied to climate science
Management and processing of large climate datasets
Long and short-term climate prediction
Ensemble characterization of climate model projections
Past (paleo) climate reconstruction.
Management and processing of large climate datasets
Long and short-term climate prediction
Ensemble characterization of climate model projections
Past (paleo) climate reconstruction.