Current and Upcoming

SEAS Colloquium in Climate Science (SCiCS)

December 6, 2018
2:45 PM - 3:45 PM
Mudd Hall, 500 W. 120 St., New York, NY 10027 214
Stephan Rasp, Meteorological Institute, Ludwig-Maximilians-University "Machine learning to represent atmospheric sub-grid processes" The representation of sub-grid processes, especially clouds, remains the largest source of uncertainty for climate prediction. Cloud-resolving models alleviate many of the gravest problems but will remain too computationally expensive for climate predictions in the coming decades. In this talk I will discuss how machine learning, and deep learning specifically, can learn to parameterize atmospheric sub-grid processes from short-term high resolution simulations. Our results tie in with a recent push towards a more data-drive climate model development.

Contact Information

APAM Department