This event is part of Confronting Global Climate Change View Details

Machine Learning for Climate and Weather Applications


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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.  


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Dimitris Giannakis New York University
Vera Hur University of Illinois at Urbana-Champaign
Jonathan Weare New York University


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