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185 * ! Mon, 8/8/2022, 2:00 PM - 3:50 PM CC-144B
Addressing Important Questions in Climate Science Using Advanced Statistical and Machine-Learning Approaches — Topic Contributed Papers
Section on Statistics and the Environment, ASA Advisory Committee on Climate Change Policy, Section on Physical and Engineering Sciences
Organizer(s): Matthias Katzfuss, Texas A&M University
Chair(s): Bo Li, University of Illinois at Urbana-Champaign
2:05 PM Prediction of Optimal Compression Settings for Spatiotemporal Climate Data Sets: Benchmarking Statistical and Machine Learning Techniques
Dorit Hammerling, Colorado School of Mines; Alexander Pinard, Colorado School of Mines; Allison Baker, National Center for Atmospheric Research
2:25 PM A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality
Mark Risser, Lawrence Berkeley National Laboratory
2:45 PM Estimating Changes in Compound Heat-Humidity Extremes: A Conditional Quantile Approach
Karen Aline McKinnon, UCLA; Andy Poppick, Carleton College
3:05 PM Multi-Model Ensemble Analysis with Neural Network Gaussian Processes
Trevor Austin Harris, Texas A&M University; Bo Li, University of Illinois at Urbana-Champaign; Ryan Sriver, University of Illinois
3:25 PM Non-Gaussian Climate Model Analysis via Scalable Bayesian Transport Maps
Matthias Katzfuss, Texas A&M University; Florian Schaefer, Georgia Tech
3:45 PM Floor Discussion