Despite tremendous advances in our understanding of the atmosphere and our capability to simulate it with numerical models on the fastest computers in the world, their remain processes that we can not accurately represent from basic physical principles. In some cases, it is an issue of computational power: we cannot resolve all relevant scales for climate prediction, from planetary scale weather systems (10^6=1,000,000’s of meters) to cloud and aerosol particles on the microscale (10^-6=0.000001 m). In other cases, we do not yet know all the relevant physics! We still need to do our best to represent these processes based on what we can simulate. Traditionally this has been done with physically motivated schemes, but there’s growing in interest in using machine learning to help. Here we take the first steps of using an artificial neural network to help parameterize atmospheric gravity waves.