Stable Machine-Learning Parameterization of Subgrid Processes with Real
Geography and Full-physics Emulation
Stable Machine-Learning Parameterization of Subgrid Processes with Real
Geography and Full-physics Emulation
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-resolution processes. A promising technique to address this is the Multiscale Modeling Framework (MMF), which embeds a small-domain, kilometer-resolution cloud-resolving model within each atmospheric column of a host climate model …