  The treatment of cloud structure in numerical weather and climate models is
often greatly simplified to make them computationally affordable. Here we
propose to correct the European Centre for Medium-Range Weather Forecasts 1D
radiation scheme ecRad for 3D cloud effects using computationally cheap neural
networks. 3D cloud effects are learned as the difference between ecRad's fast
1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy
Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them
but is about five times more computationally expensive. With typical errors
between 20 % and 30 % of the 3D signal, neural networks improve Tripleclouds'
accuracy for about 1 % increase in runtime. Thus, rather than emulating the
whole of SPARTACUS, we keep Tripleclouds unchanged for cloud-free parts of the
atmosphere and 3D-correct it elsewhere. The focus on the comparably small 3D
correction instead of the entire signal allows us to improve predictions
significantly if we assume a similar signal-to-noise ratio for both.
