2002, ‘Data Mining for the Discovery of Ocean Climate Indices

Ocean climate indices (OCIs), which are time series that summarize the behavior of selected areas of the Earth’s oceans, are important tools for predicting the effect of the oceans on land climate. In this paper we describe the use of data mining to discover Ocean Climate Indices (OCIs). In particul...

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Main Authors: Michael Steinbach, Steven Klooster, Pang-ning Tan, Christopher Potter
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Soi
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.8823
http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.421.8823 2023-05-15T17:34:32+02:00 2002, ‘Data Mining for the Discovery of Ocean Climate Indices Michael Steinbach Steven Klooster Pang-ning Tan Christopher Potter The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.8823 http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.8823 http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf shared nearest neighbor time series Earth science data correlation scientific data mining text ftciteseerx 2016-01-08T04:05:14Z Ocean climate indices (OCIs), which are time series that summarize the behavior of selected areas of the Earth’s oceans, are important tools for predicting the effect of the oceans on land climate. In this paper we describe the use of data mining to discover Ocean Climate Indices (OCIs). In particular, we apply a shared nearest neighbor (SNN) clustering algorithm to cluster the pressure and temperature time series associated with points on the ocean, yielding clusters that represent ocean regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of these ocean areas, and thus, represent potential OCIs. To evaluate cluster centroids for their usefulness as potential OCIs, we must determine which cluster centroids significantly influence the behavior of welldefined land areas. For this task, we use a variety of approaches that analyze the correlation between potential OCIs and the time series (e.g., of temperature or precipitation) which describe the behavior of land points. Based on these approaches, we have identified some cluster centroids that are almost identical to well-known OCIs, e.g., the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO). We also introduce two strategies for validating potential OCIs which do not correspond to well-known (and probably “stronger ” OCIs), namely, focusing on the correlation between “extreme ” events on the ocean and land and looking for more persistent patterns of correlation. Text North Atlantic North Atlantic oscillation Unknown Soi ENVELOPE(30.704,30.704,66.481,66.481)
institution Open Polar
collection Unknown
op_collection_id ftciteseerx
language English
topic shared nearest neighbor
time series
Earth science data
correlation
scientific data mining
spellingShingle shared nearest neighbor
time series
Earth science data
correlation
scientific data mining
Michael Steinbach
Steven Klooster
Pang-ning Tan
Christopher Potter
2002, ‘Data Mining for the Discovery of Ocean Climate Indices
topic_facet shared nearest neighbor
time series
Earth science data
correlation
scientific data mining
description Ocean climate indices (OCIs), which are time series that summarize the behavior of selected areas of the Earth’s oceans, are important tools for predicting the effect of the oceans on land climate. In this paper we describe the use of data mining to discover Ocean Climate Indices (OCIs). In particular, we apply a shared nearest neighbor (SNN) clustering algorithm to cluster the pressure and temperature time series associated with points on the ocean, yielding clusters that represent ocean regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of these ocean areas, and thus, represent potential OCIs. To evaluate cluster centroids for their usefulness as potential OCIs, we must determine which cluster centroids significantly influence the behavior of welldefined land areas. For this task, we use a variety of approaches that analyze the correlation between potential OCIs and the time series (e.g., of temperature or precipitation) which describe the behavior of land points. Based on these approaches, we have identified some cluster centroids that are almost identical to well-known OCIs, e.g., the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO). We also introduce two strategies for validating potential OCIs which do not correspond to well-known (and probably “stronger ” OCIs), namely, focusing on the correlation between “extreme ” events on the ocean and land and looking for more persistent patterns of correlation.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Michael Steinbach
Steven Klooster
Pang-ning Tan
Christopher Potter
author_facet Michael Steinbach
Steven Klooster
Pang-ning Tan
Christopher Potter
author_sort Michael Steinbach
title 2002, ‘Data Mining for the Discovery of Ocean Climate Indices
title_short 2002, ‘Data Mining for the Discovery of Ocean Climate Indices
title_full 2002, ‘Data Mining for the Discovery of Ocean Climate Indices
title_fullStr 2002, ‘Data Mining for the Discovery of Ocean Climate Indices
title_full_unstemmed 2002, ‘Data Mining for the Discovery of Ocean Climate Indices
title_sort 2002, ‘data mining for the discovery of ocean climate indices
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.8823
http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf
long_lat ENVELOPE(30.704,30.704,66.481,66.481)
geographic Soi
geographic_facet Soi
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_source http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.8823
http://www-users.cs.umn.edu/~kumar/papers/oci_clustering14.pdf
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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