Accelerating atmospheric gravity wave simulations using machine learning: Kelvin-Helmholtz instability and mountain wave sources driving gravity wave breaking and secondary gravity wave generation

We describe an initial machine learning model-the Compressible Atmosphere Model Network (CAM-Net). CAM-Net is trained on high-resolution simulations by the state-of-the-art model Complex Geometry Compressible Atmosphere Model (CGCAM).

Two initial applications to a Kelvin-Helmholtz instability source and mountain wave generation, propagation, breaking, and Secondary GW (SGW) generation in two wind environments are described here. Results show that CAM-Net can capture the key 2-D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAM-Net agree well with those from CGCAM.