  The radiative transfer equations are well-known, but radiation
parametrizations in atmospheric models are computationally expensive. A
promising tool for accelerating parametrizations is the use of machine learning
techniques. In this study, we develop a machine learning-based parametrization
for the gaseous optical properties by training neural networks to emulate a
modern radiation parameterization (RRTMGP). To minimize computational costs, we
reduce the range of atmospheric conditions for which the neural networks are
applicable and use machine-specific optimised BLAS functions to accelerate
matrix computations. To generate training data, we use a set of randomly
perturbed atmospheric profiles and calculate optical properties using RRTMGP.
Predicted optical properties are highly accurate and the resulting radiative
fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our
neural network-based gas optics parametrization is up to 4 times faster than
RRTMGP, depending on the size of the neural networks. We further test the
trade-off between speed and accuracy by training neural networks for the narrow
range of atmospheric conditions of a single large-eddy simulation, so smaller
and therefore faster networks can achieve a desired accuracy. We conclude that
our machine learning-based parametrization can speed-up radiative transfer
computations whilst retaining high accuracy.
