Joint Seminar: Machine learning for the parameterization of atmospheric 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 are computationally expensive. In this talk I will show how a deep neural network can learn to parameterize atmospheric sub-grid processes from a short-term high-resolution dataset. Our results tie in with a recent push towards a more data-driven climate model development.

Datum

17.07.2018

Uhrzeit

15:15 Uhr

Ort

Bundesstr. 53, room 022/023
Seminar Room 022/023, Ground Floor, Bundesstrasse 53, 20146 Hamburg, Hamburg

ReferentIn

Stephan Rasp, LMU München

Chair

Bjorn Stevens

Zur Übersicht