Statistics and Machine Learning for Climate Science


Description:

This session seeks presentations related to the use of statistics an machine learning in Climate Science.  It is Joint between the 28th Conference on Probability and Statistics, 23st Conference on Artificial Intelligence for Environmental Science, and the 35th Conference on Climate Variability and Change


Papers:
The Climate Change Deniers are Wrong: Evidence from NOAA's Barrow Atmospheric Observatory in Alaska
Kevin F Forbes, Energy and Environmental Data Science, Malahide, D, Ireland

A Subseasonal U.S. Tornado Outbreak Index: Empirical Relationship between Outbreak-level Tornado Occurrence and the Large-scale Environment
Kelsey M. Malloy, Columbia University, New York, NY; Columbia University, New York, NY; and M. K. Tippett

Constraining the Simulated Radiative Effects of Biomass Burning in Southern Africa
Victor Alejandro Sanchez, Carnegie Mellon University, Pittsburgh, PA; Carnegie Mellon University, Pittsburgh, PA; and J. Carzon, M. Kuusela, and H. Gordon

Parameterizing Cloud Microphysics with Machine Learning-Enabled Bayesian Parameter Inference
Kaitlyn Loftus, Columbia University, New York, NY; and M. van Lier-Walqui, H. Morrison, K. K. Chandrakar, PhD, M. A. Bhouri, and S. P. Santos

Submission Deadline: Thursday, 31 August 2023



See more of: 28th Conference on Probability and Statistics
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