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Detecting Extreme Events from Climate Time Series via Topic Modeling

  • Cheng TangEmail author
  • Claire Monteleoni

Abstract

We propose a topic-model-based approach to define and detect patterns corresponding to extreme climate-related events over different regions around the globe from the time series data of various climate variables. While topic models are popular for tasks such as natural language processing, bioinformatics, and computer vision, we are unaware of their applications to modeling climate extremes. Inference from our model can be used to construct climate extreme indices, predict disastrous extreme events such as drought and floods, and understand the influence of climate change on climate extremes.

Keywords

Climate extremes Extreme events Topic modeling Latent Dirichlet allocation Unsupervised learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.George Washington UniversityWashington, DCUSA

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