Advanced Data Clustering Applied to Climate Model Intercomparison

Richard Hyde, Amber A. Leeson, Ryan Hossaini

Research output: Contribution to conferencePoster

Abstract

Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets in a fast and computationally-efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) may yet to be fully realised. In this poster, we explore the specific application of clustering as a mechanism for sub-sampling a climate model ensemble in order to identify outliers and achieve a deeper understanding of inter-model variability than is possible using traditional methods, which tend to be somewhat rudimentary.
Original languageEnglish
Number of pages1
Publication statusPublished - 13 Apr 2018
EventEGU General Assembly 2018 - Vienna, Austria
Duration: 4 Apr 201813 Apr 2018

Conference

ConferenceEGU General Assembly 2018
Abbreviated titleEGU2018
CountryAustria
CityVienna
Period4/04/1813/04/18

ASJC Scopus subject areas

  • Computer Science(all)
  • Earth and Planetary Sciences(all)

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    Hyde, R., Leeson, A. A., & Hossaini, R. (2018). Advanced Data Clustering Applied to Climate Model Intercomparison. Poster session presented at EGU General Assembly 2018, Vienna, Austria.