This event is part of Confronting Global Climate Change View Details

Machine Learning for Climate and Weather Applications

Description

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The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds to thousands of kilometers and centuries.  Machine learning approaches present a timely opportunity to leverage the information content of large datasets generated by observational systems and models to improve scientific understanding and prediction capability of weather and climate dynamics. The workshop will bring together an interdisciplinary group of researchers in applied mathematics, climate science, and data science to discuss recent advances and future perspectives on machine learning for weather and climate applications, including feature extraction, subgrid-scale modeling, and statistical prediction.  

Organizers

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D G
Dimitris Giannakis Dartmouth University
V H
Vera Hur University of Illinois at Urbana-Champaign
J W
Jonathan Weare New York University

Speakers

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D A
Dorian Abbot University of Chicago
E B
Elizabeth Barnes Colorado State University
T B
Tom Beucler University of Lausanne
F B
Freddy Bouchet Centre National de la Recherche Scientifique (CNRS)
N B
Noah Brenowitz Allen Institute for Artificial Intelligence
N C
Nan Chen University of Wisconsin-Madison
O D
Oliver Dunbar Caltech
J F
Justin Finkel University of Chicago
G F
Gary Froyland University of New South Wales
A G
Auroop Ganguly Northeastern University
P G
Pierre Gentine Columbia University
I G
Ian Grooms University of Colorado
P H
Pedram Hassanzadeh Rice University
K K
Karthik Kashinath Lawrence Berkeley National Laboratory
V L
Valerio Lucarini University of Reading
P M
Peetak Mitra Xerox Palo Alto Research Center
D Q
Di Qi Purdue University
T S
Themistoklis Sapsis MIT
A S
Aditi Sheshadri Stanford University
M S
Maike Sonnewald Princeton University
M Y
Minah Yang New York University
J Y
Janni Yuval MIT

Registration

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