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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 , https://doi.org/10.1175/1520-0450(2001)040<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 , https://doi.org/10.1002/qj.49712656318 . 10.1002/qj.49712656318 Chong , E. , C. Han , and F. C. Park , 2017 : Deep learning networks for

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