Climate Similarity Search: GeoWeb Tool for Exploring Climate Variability

Pawel Netzel Space Informatics Laboratory, University of Cincinnati, Cincinnati, Ohio

Search for other papers by Pawel Netzel in
Current site
Google Scholar
PubMed
Close
and
Tomasz Stepinski Space Informatics Laboratory, University of Cincinnati, Cincinnati, Ohio

Search for other papers by Tomasz Stepinski in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Tomasz Stepinski, stepintz@uc.edu

Abstract

Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Tomasz Stepinski, stepintz@uc.edu
Save
  • Berndt, D. J., and J. Clifford, 1994: Using dynamic time warping to find patterns in time series. Workshop on Knowledge Discovery in Databases, Seattle, WA, AAAI Tech. Rep. WS-94–03, 359–370.

  • Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, 2005: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 19651978, https://doi.org/10.1002/joc.1276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World map of the Köppen-Geiger climate classification updated. Meteor. Z., 15, 259263, https://doi.org/10.1127/0941-2948/2006/0130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Netzel, P., and T. F. Stepinski, 2016: On using a clustering approach for global climate classification. J. Climate, 29, 33873401, https://doi.org/10.1175/JCLI-D-15-0640.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1419 513 30
PDF Downloads 979 306 25