Use of a neural‐network‐based long‐wave radiative‐transfer scheme in the ECMWF atmospheric model

Abstract The definition of an approach for radiative‐transfer modelling that would enable computation limes suitable for climate studies and a satisfactory accuracy, has proved to be a challenge for modellers. A fast radiative‐transfer model is tested at ECMWF: NeuroFlux. It is based on an artificial neural‐network technique used in conjunction with a classical cloud approximation (the multilayer grey‐body model). The accuracy of (he method is assessed through code‐by‐code comparisons, climate simulations and ten‐day forecasts with the ECMWF model. The accuracy of NeuroFlux appears to be comparable to the accuracy of the ECMWF operational scheme, with a negligible impact on the simulations, while its computing time is seven times faster.