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Climate Informatics 2016 Workshop Proceedings
The workshop proceedings are available in NCAR's OpenSky repository: http://dx.doi.org/10.5065/D6K072N6
How to cite these proceedings:
A. Banerjee, W. Ding, J. Dy, V. Lyubchich, A. Rhines (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.), Proceedings of the 6th International Workshop on Climate Informatics: CI 2016. NCAR Technical Note NCAR/TN-529+PROC, Sept 2016, 159 pp., doi: 10.5065/D6K072N6.
The ISBN number (optional use in citation) is 978-0-9973548-1
About Climate Informatics
We have greatly increased the volume and diversity of climate data from satellites, environmental sensors and climate models in order to improve our understanding of the climate system. However, this very increase in volume and diversity can make the use of traditional analysis tools impractical and necessitate the need to carry out knowledge discovery from data. Machine learning has made significant impacts in fields ranging from web search to bioinformatics, and the impact of machine learning on climate science could be as profound. However, because the goal of machine learning in climate science is to improve our understanding of the climate system, it is necessary to employ techniques that go beyond simply taking advantage of co-occurence, and, instead, enable increased understanding.
The Climate Informatics workshop series seeks to build collaborative relationships between researchers from statistics, machine learning and data mining and researchers in climate science. Because climate models and observed datasets are increasing in complexity and volume, and because the nature of our changing climate is an urgent area of discovery, there are many opportunities for such partnerships.
The conference logo image is courtesy of Michael Tippett. Colors show deviations of sea-surface temperatures from their climatological values in the equatorial Pacific from January 1997 to April 2000 with time going counter-clockwise.