
Talk abstract:
Global atmospheric `storm-resolving' models with horizontal grid spacing of less than ~5km resolve deep cumulus convection and flow in complex terrain. While computationally expensive, they can be run for yearlong scales and can serve as reference models for improving more economical coarse-grid global weather and climate models. Machine learning (ML) offers an avenue for translating the patterns seen in storm-resolving models onto the coarser grid, with the ultimate goal of reducing uncertainties in regional precipitation and temperature trends in global…