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Noah D. Brenowitz, Tom Beucler, Michael Pritchard, and Christopher S. Bretherton

) pioneered this growing subfield by training emulators of atmospheric radiation parameterizations. O’Gorman and Dwyer (2018) trained a random forest (RF) to emulate the convection scheme of an atmospheric GCM and were able to reproduce its equilibrium climate. More recently, neural networks (NNs) have been trained to predict the total heating and moistening of more realistic datasets including the Superparameterized Community Atmosphere Model (SPCAM) ( Rasp et al. 2018 ; Gentine et al. 2018 ) and a

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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

( Krasnopolsky 2013 ), the most important being to achieve high performance within the host NWP model. Fast emulations of existing model physics parameterizations are usually developed for complex parameterizations that are computational bottlenecks, such as atmospheric radiation parameterizations and the planetary boundary layer (e.g., Wang et al. 2019 ). Krasnopolsky (2019) demonstrated that a 0.1 K day −1 RMS accuracy can be obtained for varied individual instantaneous profiles with shallow NN

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