Climate Tails

Empowering Climate Science Through Effective Tail Learniing


Project Aims:

  1. Establish the statistical foundation for integrated-quantile regression and expected shortfall.
  2. Develop new tail regression methods to estimate climate extremes.
  3. Explore general tail measures of joint climate extremes.
  4. Develop and maintain comprehensive platform of climate risk predictions.


Senior Personnel:

  • Bo Li, Department of Statistics, University of Illinois at Urbana-Champaign
    • Spatiotemporal and environmental statistics
  • Huixia Judy Wang, Department of Statistics, George Washington University
    • quantile regression, extreme value theory, semiparametric regression, spatial analysis, highdimensional inference
  • Kean Ming Tan, Department of Statistics, University of Michigan
    • Multivariate statistical methods development
  • Ryan Sriver, Department of Atmospheric Sciences, University of illinois at Urbana-Champaign
    • Climate and weather extremes
  • Wenxin Zhou, Department of Information and Decision Sciences, University of Illinois at Chicago
    • High-dimensional data analysis


Recent Highlights:

Undergraduate researcher Ziyi Zhou is developing new expected shortfall techniques to analyze how extreme temperature and precipitation are changing with climate.  The figure below shows seasonal trends in the >90% tail of daily temperature distributions since 1940, highlighting increases in summer extremes within much of the western US.