Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-permitting Simulations for Data-driven Parameterizations

Gravity waves (GWs) present a challenge to climate prediction: waves on scales of $O( 1~km)$ to $O(100~km)$ can neither be systematically measured with conventional observational systems, nor properly represented (resolved) in operational climate models, which have a typical grid spacing on the order of 100 $km$. Therefore, in these climate models, small-scale GWs must be $parameterized$, or estimated, based on the resolved (large-scale) flow. The primary effects of these small-scale waves on the resolved flow is the so-called sub-grid  scale (SGS) drag (GWD), resulting from the propagation and breaking of these waves. Existing SGS parameterizations for GWD in general circulation models (GCMs) are all highly simplified; e.g., they only account for vertical propagation of GWs. With growing computing power, a promising alternative approach is to use machine learning to develop data-driven parameterizations. However, this requires to first generate reliable high-resolution computer simulations and then extract GWD from these simulations. This study follows these steps, compares different extraction methods, and describes some challenges and pathways to make advances. Furthermore, our results suggest that the horizontal propagation of GWs should be included in parameterizations too, however, extra care is needed in order to extract the resulting GWD from high-resolution data.