Understanding dominant factors for precipitation over the great lakes region

Soumyadeep Chatterjee, Stefan Liess, Arindam Banerjee, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Statistical modeling of local precipitation involves understanding local, regional and global factors informative of precipitation variability in a region. Modern machine learning methods for feature selection can potentially be explored for identifying statistically significant features from pool of potential predictors of precipitation. In this work, we consider sparse regression, which simultaneously performs feature selection and regression, followed by random permutation tests for selecting dominant factors. We consider average winter precipitation over Great Lakes Region in order to identify its dominant influencing factors. Experiments show that global climate indices, computed at different temporal lags, offer predictive information for winter precipitation. Further, among the dominant factors identified using randomized permutation tests, multiple climate indices indicate the influence of geopotential height patterns on winter precipitation. Using composite analysis, we illustrate that certain patterns are indeed typical in high and low precipitation years, and offer plausible scientific reasons for variations in precipitation. Thus, feature selection methods can be useful in identifying influential climate processes and variables, and thereby provide useful hypotheses over physical mechanisms affecting local precipitation.

Original languageEnglish (US)
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016


Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States

Bibliographical note

Funding Information:
We would like to thank Debasish Das for help with the climate data. This research was supported in part by NSF Grants IIS-1029711, IIS-0916750, SES-0851705, IIS- 0812183, and NSF CAREER Grant IIS-0953274. We are grateful for technical support from University of Minnesota Supercomputing Institute (MSI).

Publisher Copyright:
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Copyright 2017 Elsevier B.V., All rights reserved.


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