Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities

James H. Faghmous, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingChapter

51 Scopus citations


Our planet is experiencing simultaneous changes in global population, urbanization, and climate. These changes, along with the rapid growth of climate data and increasing popularity of data mining techniques may lead to the conclusion that the time is ripe for data mining to spur major innovations in climate science. However, climate data bring forth unique challenges that are unfamiliar to the traditional data mining literature, and unless they are addressed, data mining will not have the same powerful impact that it has had on fields such as biology or e-commerce. In this chapter, we refer to spatio-temporal data mining (STDM) as a collection of methods that mine the data’s spatio-temporal context to increase an algorithm’s accuracy, scalability, or interpretability (relative to non-space-time aware algorithms).We highlight some of the singular characteristics and challenges STDM faces within climate data and their applications, and provide the reader with an overview of the advances in STDM and related climate applications. We also demonstrate some of the concepts introduced in the chapter’s earlier sections with a real-world STDM pattern mining application to identify mesoscale ocean eddies from satellite data. The case-study provides the reader with concrete examples of challenges faced when mining climate data and how effectively analyzing the data’s spatio-temporal context may improve existing methods’ accuracy, interpretability, and scalability. We end the chapter with a discussion of notable opportunities for STDM research within climate.

Original languageEnglish (US)
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages34
StatePublished - 2014

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Bibliographical note

Funding Information:
Part of the research presented in this chapter was funded by an NSF Graduate Research Fellowship, an NSF Nordic Research Opportunity Fellowship, a University of Minnesota Doctoral Dissertation Fellowship, and an NSF Expeditions in Computing Grant (IIS-1029711). Access to computing resources was provided by the University of Minnesota Supercomputing Institute. The authors thank Varun Mithal for generating Figure 4 and Dr. Stefan Sobolowski for generating Figure 7. We also thank Dr. Stefan Liess for constructive comments that improved the quality of the manuscript.

Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.


  • Climate Data
  • Cyclonic Eddy
  • Land Surface Temperature
  • Pattern Mining
  • Vertical Wind Shear


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