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Ricardo Martins Campos, Vladimir Krasnopolsky, Jose-Henrique G. M. Alves, and Stephen G. Penny

1. Introduction The U.S. National Centers for Environmental Prediction (NCEP) have produced atmospheric forecasts using ensembles since 1992 and wave ensembles since 2005. Kalnay (2003) describes the two main advantages of using ensemble forecasts: the ensemble members tend to smooth out uncertain components, which lead to better skill than single deterministic forecasts; and the spread of the ensemble members provides an estimation of the uncertainty. The mean of the ensemble members is

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

emulators with O (100) neurons. Even in moderate resolution climate models, the calculation of the atmospheric radiation can consume more than 50% of the computational load. ML emulations of atmospheric radiation parameterizations accelerate calculation of the long wave radiation about 16 times and the shortwave radiation about 60 times ( Krasnopolsky 2019 ). Enhanced parameterization by training on an advanced model ML techniques can also be used not only to emulate existing physics parameterizations

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

– 1461 ,<1445:EOTJOI>2.0.CO;2 . 10.1175/1520-0450(2001)040<1445:EOTJOI>2.0.CO;2 Chevallier , F. , J.-J. Morcrette , F. Chéruy , and N. A. Scott , 2000 : Use of a neural-network-based long-wave radiative-transfer scheme in the ECMWF atmospheric model . Quart. J. Roy. Meteor. Soc. , 126 , 761 – 776 , . 10.1002/qj.49712656318 Chong , E. , C. Han , and F. C. Park , 2017 : Deep learning networks for

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