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

between humidity/temperature and heating/moistening, or likewise by automatic differentiation of nonlinear regression models ( Brenowitz and Bretherton 2019 ). Visualizing the LRF as a matrix does not predict the consequences of coupling the scheme to atmospheric fluid mechanics (i.e., the GCM’s “dynamical core”). Kuang (2010) takes this additional step by coupling CRM-derived LRFs with linearized gravity wave dynamics and further developing a vertically truncated ordinary differential equation

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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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John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

supercell thunderstorms ( Apke et al. 2016 ). Textural patterns at cloud top have also been used to infer updraft strength ( Bedka and Khlopenkov 2016 ). In the presence of strong upper-level flow, some overshoots generate above-anvil cirrus plumes (AACP) downstream from the overshooting top as a result of internal gravity wave breaking and are apparent in visible satellite imagery ( Wang 2003 ; Wang et al. 2016 ; Homeyer et al. 2017 ; Bedka et al. 2018 ). AACPs in visible imagery are responsible for

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

: Tensorflow: A system for large-scale machine learning . 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI’16) , Savannah, GA, USENIX Association , 265 – 283 . Anderegg , W. R. , and Coauthors , 2015 : Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models . Science , 349 , 528 – 532 , . 10.1126/science.aab1833 Andrews , D. G. , 2010 : An Introduction to Atmospheric Physics . Cambridge

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