  The treatment of cloud structure in radiation schemes used in operational
numerical weather prediction and climate models is often greatly simplified to
make them computationally affordable. Here, we propose to correct the radiation
scheme ecRad -- as used for operational predictions at the European Centre for
Medium-Range Weather Forecasts -- for 3D cloud effects using computationally
cheap neural networks. 3D cloud effects are learned as the difference between
ecRad's fast Tripleclouds solver that neglects them, and its SPeedy Algorithm
for Radiative TrAnsfer through CloUd Sides (SPARTACUS) solver that includes
them but increases the cost of the entire radiation scheme by around five times
so is too expensive for operational use. We find that the emulator can be used
to increase the accuracy of both longwave and shortwave fluxes of Tripleclouds,
with a bulk mean absolute error on the order of 20 and 30 % the 3D signal, for
less than 1 % increase in computational cost. By using the neural network to
correct the cloud-related errors in a fast radiation scheme, rather than trying
to emulate the entire radiation scheme, we take advantage of the fast scheme's
accurate performance in cloud-free parts of the atmosphere, particularly in the
stratosphere and mesosphere.
