Sack Lunch Seminar (SLS)

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 be used to build a data-driven subgrid parameterization from short-term high-resolution data. Our results tie in with a recent push towards data-driven climate model development.