  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 to 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. {For medium range forecasting we find evidence our emulators are
more accurate} than the version of the parametrisation scheme that is used for
operational predictions. 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.
