GTC Silicon Valley-2019: Machine Learning Parameterizations of Atmospheric Processes
GTC Silicon Valley-2019 ID:S9245:Machine Learning Parameterizations of Atmospheric Processes
David Gagne(National Center for Atmospheric Research)
The numerical simulations underlying our weather forecasts and climate projections depend on a large set of sub-models called parameterization schemes to represent unresolved processes in the Earth system. Many existing parameterizations are computationally intensive or contain simplifying assumptions that lead to biases or artifacts in the simulations. We'll talk about how machine learning models can address both problems by emulating a more complex parameterization or learning to represent a process from long records of detailed observations. We will describe machine learning parameterizations of the conversion of cloud water to rain and the transfer of energy between the surface and atmosphere, and we'll compare these against existing approaches for these problems.