Physical Sciences and Mathematics Commons^™

Accelerating Atmospheric Gravity Wave Simulations Using Machine Learning: Kelvin-Helmholtz Instability And Mountain Wave Sources Driving Gravity Wave Breaking And Secondary Gravity Wave Generation, Wenjun Dong, David Fritts, Alan Z. Liu, Hanli Liu, Jonathan Snively

Publications

Gravity waves (GWs) and their associated multi-scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. We describe an initial, two-dimensional (2-D), machine learning model – the Compressible Atmosphere Model Network (CAMNet) - intended as a first step toward a more general, three-dimensional, highly-efficient, model for applications to nonlinear GW dynamics description. CAMNet employs a physics-informed neural operator to dramatically accelerate GW and secondary GW (SGW) simulations applied to two GW sources to date. CAMNet is trained on high-resolution simulations by the state-of-the-art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two …