Journal
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Volume 126, Issue 563, Pages 761-776Publisher
ROYAL METEOROLOGICAL SOC
DOI: 10.1002/qj.49712656318
Keywords
artificial neural networks; general-circulation models; long-wave radiative transfer
Categories
Ask authors/readers for more resources
The definition of an approach for radiative-transfer modelling that would enable computation times 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 the 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available