KITP Program: Machine Learning and the Physics of Climate
(Nov 1 - Dec 17, 2021)
Coordinators: Annalisa Bracco, Henk A. Dijkstra, Claire Monteleoni, and Laure Zanna

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Speakers: Please contact us about file upload for your slides.

Time Speaker Title
11/1-4 Conference: Machine Learning for Climate
11/08, 9:00am Dmitri Kondrashov
Data-driven stochastic climate modeling and prediction[Slides]
11/10, 9:00am Deborah Khider
The challenges of using paleoclimate data for decadal prediction[Slides][Video][CC]
11/15, 9:30am Brian White
Deep learning applications for climate and weather modeling: toward improvements in speed, resolution and scenario generation[Embargoed]
11/17, 9:30am Andreas Gerhardus
Learning cause-and-effect relationships from time series data[Video]
11/22, 9:30am Bia Villas Boas
Colorado School of Mines/Caltech
From noise to signal: what surface waves can teach us about currents[Video]
11/24, 9:00am Raffaele Ferrari
New approaches to calibration of parameterizations of boundary layer turbulence[Video]
11/29, 9:30am Freddy Bouchet
ENS Lyon
Predicting extreme heat waves using rare event simulations and deep neural networks[Video]
12/01, 9:30am Markus Abel
Ambrosys GmbH
Symbolic regression and mathematical postprocessing for machine learning of (climate) dynamics[Video]
12/06, 9:30am Julien Brajard
Bridging observations and numerical modeling using machine learning
12/06, 11:30am Erik Mulder
Univ. of Gronigen
Symbiotic ocean modeling using physics-controlled Echo State Networks
12/08, 9:30am Alex Robel
Georgia Tech
Statistical learning of climate for large ensemble ice sheet simulations
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