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Tutorial
Machine Learning and Statistics for Climate Science
Karen A McKinnon · Andrew N Poppick

Mon Dec 06 01:00 PM -- 05:00 PM (PST) @ Virtual

The assessment of climate variability and change is enriched by novel applications of statistics and machine learning methodologies. This tutorial will be an introduction to some of the common statistical and machine learning problems that arise in climate science. The goal is to give attendees a sense of the intersections between the fields and to help promote future interdisciplinary collaborations. We will introduce you to different climate data sources (e.g., in situ measurements, satellite data, climate model data, etc.) and discuss problems including: characterizing changes in extreme events like heatwaves or extreme precipitation, summarizing high-dimensional spatiotemporal climate data, and using statistical methods to predict climate variability and potentially improve future projections. The focus will be on methodological applications; we will discuss both core methodologies and recent innovations. Prior knowledge of climate science is not assumed and we will emphasize the value of engaging substantively with domain experts.

Author Information

Karen A McKinnon (University of California Los Angeles)

Karen McKinnon is an Assistant Professor of Statistics and the Environment at UCLA. She received her PhD in Earth and Planetary Sciences at Harvard University in 2015. Before joining UCLA in 2018, she was an Advanced Study Postdoc at the National Center for Atmospheric Research and an Applied Scientist at Descartes Labs. Her research focuses on large-scale climate variability and change, with a particular interest in extreme events, and draws upon a diverse toolbox that spans climate dynamics, statistics, and machine learning.

Andrew N Poppick (Carleton College)

Andrew Poppick is an Assistant Professor of Statistics at Carleton College. He received his Ph.D. in Statistics in 2016 from the University of Chicago. His research is primarily in statistical applications to climate, especially characterizing temporal dependence and nonstationarity in climate processes.

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