Speaker: PhD student Stephan Rasp from Ludwig Maximilians University
Title: "Machine learning to represent atmospheric sub-grid processes."
Abstract: 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.
LOCATION:HUCE Seminar Room MCZ 440 STATUS:CONFIRMED DTSTART:20181205T170000Z DTEND:20181205T170000Z END:VEVENT END:VCALENDAR