  We assess the value of machine learning as an accelerator for the
parameterisation schemes of operational weather forecasting systems,
specifically the parameterisation of non-orographic gravity wave drag.
Emulators of this scheme can be trained that produce stable and accurate
results up to seasonal forecasting timescales. Generally, more complex networks
produce more accurate emulators. By training on an increased complexity version
of the existing parameterisation scheme we build emulators that produce more
accurate forecasts than the existing parameterisation scheme. Using the current
operational CPU hardware our emulators have a similar computational cost to the
existing scheme, but are heavily limited by data movement. On GPU hardware our
emulators perform ten times faster than the existing scheme on a CPU.
