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

( Cintineo et al. 2018 ). Convolutional neural networks (CNN) are specially designed to learn from spatial grids and often contain many layers, which qualifies them as a deep-learning method (section 1.1.4 of Chollet 2018 ). In traditional ML, spatial grids must be transformed into scalar features, which become the direct inputs to the model. Examples are principal components, spatial statistics (such as means and standard deviations), and raw gridpoint values (where each value in the grid is treated as

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

thunderstorms into a single output, we utilize a deep-learning approach that mimics expert human pattern recognition of intense convection in satellite imagery. The goal of this approach is to quantify convective intensity automatically, saving forecasters time in identifying, diagnosing, and prioritizing threats. Deep learning is a branch of machine-learning methods based on artificial neural networks with feature learning, or the ability to automatically find salient features in data (e.g., Schmidhuber

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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

popular and widely used CNNs, were trained with millions of images and are highly accurate ( Krizhevsky et al. 2017 ; Simonyan and Zisserman 2015 ). Deep learning (DL) is a term used to describe the optimization of NNs with multiple layers. Deep networks tend to be large relative to other ML models and their training typically requires powerful graphics processing unit (GPU) acceleration in order to make training practical. In comparison with other approaches, deep CNNs are particularly effective for

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

1. Introduction Deep learning (DL) is a newly popular, powerful and often confounding computational tool for developing predictive models in the sciences. It builds on a long legacy of neural network modeling, with a key feature being the organization of neural connections into multiple layers of nonlinear operations, enabling models to apply high levels of abstraction in their tasks. New hardware innovations, particularly in accessing graphical processing units (GPUs), have enabled DL

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Imme Ebert-Uphoff and Kyle Hilburn

) for deeper study. From simple neurons to powerful neural networks. An artificial neural network (ANN; or NN for short) is a machine learning method loosely inspired by the human brain. An NN consists of a set of neurons (aka nodes ) that are connected by synapses which pass signals between the neurons. In sequential NNs , which are the primary type considered here, all neurons are arranged in a sequence of layers, and signals pass in a one-directional manner from the input layer through

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

. Miller , 2020 : Evaluating Geostationary Lightning Mapper flash rates within intense convective storms . J. Geophys. Res. Atmos. , 125 , e2020JD032827, https://doi.org/10.1029/2020JD032827 . 10.1029/2020JD032827 Samsi , S. , C. J. Mattioli , and M. S. Veillette , 2019 : Distributed deep learning for precipitation nowcasting. IEEE High Performance Extreme Computing Conf. , Waltham, MA, IEEE, https://doi.org/10.1109/HPEC.2019.8916416 . 10.1109/HPEC.2019.8916416 Sawada , Y. , K

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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

learning method could benefit from considering multiple processes simultaneously as opposed to the best-separating field presented here (a task outside of the scope of this project). Typically, the best-separating GFSA fields were consistent among multiple RI definitions, revealing humidity across a deep vertical layer, low-level instability, and midlevel vorticity as potentially important separating fields for RI/non-RI environments. Ultimately, the improved separability offered by the presented

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

, including a machine-learning parameterization, should capture the dependence of convection to these parameters. One such parameter is the LTS: LTS = θ ⁡ ( 700   hPa ) − SST , where θ is the potential temperature and SST is the sea surface temperature. Low LTS indicates the lower troposphere is conditionally unstable, favoring deep convection. A second controlling parameter is the midtropospheric moisture, defined by Q = ∫ 850 hPa 550 hPa q T   d p g . Cumulus updrafts entrain surrounding air as they

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Amy McGovern, Ryan Lagerquist, David John Gagne II, G. Eli Jergensen, Kimberly L. Elmore, Cameron R. Homeyer, and Travis Smith

Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed. Machine learning (ML) and deep learning (DL; LeCun et al. 2015 ) have recently achieved breakthroughs across a variety of fields, including the world’s best Go player ( Silver et al. 2016 , 2017 ), medical diagnosis ( Rakhlin et al. 2018 ), and galaxy classification ( Dieleman et al. 2015 ). Simple forms of ML (e.g., linear regression) have been used in meteorology since at

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

(e.g., Gagne et al. 2017 ; Campos et al. 2019 ). It is now clear (see references cited as examples in the body of this essay) that AI approaches, including recent advances in ML technology, such as Transfer Learning and Long and Short Term Memory Networks (LSTMs; Hochreiter and Schmidhuber 1997 ), Deep and Extreme Learning ( Schmidhuber 2015 ; Goodfellow et al. 2019 ), and Computer Vision, have the potential to meet increasing requirements for and by nowcast and forecast products, including

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