期刊
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
卷 126, 期 563, 页码 761-776出版社
ROYAL METEOROLOGICAL SOC
DOI: 10.1002/qj.49712656318
关键词
artificial neural networks; general-circulation models; long-wave radiative transfer
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.
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