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

, resulting in overshooting cloud tops. These features may block strong upper-level wind flow, which is diverted around the overshooting tops, carrying cloud debris from the updraft summit, resulting in U- or V-shaped thermal couplets in infrared brightness temperature imagery (e.g., Setvák et al. 2013 ; Wang 2007 ; Brunner et al. 2007 ). Furthermore, high-refresh sequences of geostationary satellite images have been used to retrieve cloud-top divergence and cloud-top vorticity and subsequently detect

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

near-global cloud-resolving model (GCRM) ( Brenowitz and Bretherton 2018 , 2019 ). Most ML parameterizations are deterministic, a potentially harmful approximation ( Palmer 2001 ), but stochastic extensions of these techniques have been proposed ( Krasnopolsky et al. 2013 ). RFs appear robust to coupling: their output spaces are bounded since their predictions for any given input are averages over actual samples in the training data ( O’Gorman and Dwyer 2018 ). In contrast, NNs are often

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Ryan Lagerquist, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith

except the last follow this linear transformation with leaky ReLU and batch normalization, like the convolutional layers. The last dense layer uses the sigmoid activation function (section 6.2.2.2 of Goodfellow et al. 2016 ), which forces the output to range over [0, 1], allowing it to be interpreted as a probability. The last dense layer does not use batch normalization, because this would force the outputs to a Gaussian distribution, which permits values outside [0, 1] and is therefore invalid for

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

final section concludes with a summary and outlook. SATELLITE REMOTE SENSING AND NWP CHALLENGES AND ML. The many challenges to satellite remote sensing and NWP are grouped here by forcing mechanism—Big Data, advanced models and applications, and user demands. In the sections that follow we show that recent advances in ML in terms of efficiency, capability, and ease of implementation, can help to meet these challenges. First, NWP is failing to exploit the growing diversity and volume of observations

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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

interference of dense plant canopy and limitations in the depth of detection within the soil column ( Entekhabi et al. 2014 ), as well as sensor limitations (especially before the use of L-band radiometers). Likewise, the fidelity of LSM simulations may be undermined by model errors (e.g., model structure error, model parameter error), forcing data uncertainties and limited usage of ground-based observations for model calibration and validation ( Xia et al. 2014 ). Thus, efforts that aim to improve large

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Eric D. Loken, Adam J. Clark, Amy McGovern, Montgomery Flora, and Kent Knopfmeier

with the proper value of σ , spatially smoothing ensemble probabilities reduces sharpness (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ) and potentially sacrifices resolution if too much smoothing is required. Moreover, the “best” value of σ may vary based on geographic location and time of year (e.g., Fig. 3 ), as precipitation uncertainty is reduced where stronger and/or more predictable forcing is present, such as near high terrain (e.g., Blake et al. 2018 ) or during the

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