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

operational analysis product, limiting applicability of their results to real-time forecasting. Rather than focus on processes exclusively, previous studies (e.g., Kaplan et al. 2015 ) showed some success employing statistical hypothesis testing to identify relevant diagnostic predictors (derived from previous physical understanding) that can distinguish between RI and non-RI environments. Predictors that showed the greatest discrimination capability are currently used operationally by the NHC in a

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

climate change ( Schneider et al. 2017 ). Owing to advances in both computing and available datasets, machine learning (ML) is now a viable alternative for traditional parameterization. Viewed from the perspective of ML, parameterization is a straightforward regression problem. A parameterization maps a set of inputs, namely, atmospheric profiles of humidity and temperature, to some outputs, profiles of subgrid heating and moistening. Krasnopolsky et al. (2005) and Chevallier et al. (1998

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

in separate files. Based on the National Weather Service (NWS)-defined severe criteria of hail diameter ≥ 1 in. (25.4 mm), wind gust ≥ 50 kt (25.72 m s −1 ), or the presence of a tornado, 55.5% of the intense class images were from severe storms (irrespective of when a severe report occurred), while only 5.6% of the ordinary class images were from severe storms. This analysis confirms that storms that exhibit one or more of the storm top features (e.g., overshooting tops, cold-U, cloud

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

analysis method combining LRP ( section 3d ) together with target architecture experiments ( section 3b ) and synthetic inputs ( section 3e ) to gain insights on strategies learned by the ML model that produce good skill. The ML model developed in this paper is envisioned for DA applications, but there are other related research efforts with aviation and nowcasting applications. Veillette et al. (2018) derived a CNN to predict radar vertically integrated liquid (VIL) from satellite data for aviation

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Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

event, it becomes a challenge to find sufficient number of analogs if the reforecast archive is not sufficiently long enough ( Hamill et al. 2015 ). There are alternative approaches that are less reliant on the size of the training data. The logistic regression method is one of these methods and has been found suitable for dealing with medium-range precipitation forecasts in several regions ( Wilks 2006 ; Wilks and Hamill 2007 ). Few previous studies have compared the relative performance of analog

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

-0242.1 . 10.1175/MWR-D-15-0242.1 Bergthórsson , P. , and B. Döös , 1955 : Numerical weather map analysis . Tellus , 7 , 329 – 340 , . 10.3402/tellusa.v7i3.8902 Billet , J. , M. DeLisi , B. Smith , and C. Gates , 1997 : Use of regression techniques to predict hail size and the probability of large hail . Wea. Forecasting , 12 , 154 – 164 ,<0154:UORTTP>2.0.CO;2 . 10

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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

Sounder (ATMS) data with traditional iterative-based systems, the Multi-Instrument Inversion and Data Assimilation Preprocessing System–AI (MIIDAPS-AI) approach (excluding I/O and training time) requires only 5 s of CPU time. Highlights of activities to leverage AI in data assimilation AI has the potential to benefit DA in all stages of the analysis–forecast cycle. The process steps in a typical DA cycle include producing a forecast with a large-scale nonlinear numerical model, preprocessing vast

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

manipulate uncertainty about models and predictions. Gaussian process regression (GPR; Pasolli et al. 2010 ) is roughly speaking the ML version of variational assimilation. A natural output of the GPR algorithm is the uncertainty of its estimate, a quantity that is difficult to obtain from ordinary variational analysis approaches. With regard to reproducibility, note that AI techniques eliminate some forms of human error inherent in many traditional DA techniques, in the sense that these traditional

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

machine learning has had a long and fruitful application to meteorology (see Haupt et al. 2008 ; McGovern et al. 2017 and references therein), the state of the science of DL applied to meteorology is limited but rapidly growing. A large portion of work in this field to date applies to short-term forecasting for renewable energy ( Diaz et al. 2015 ; Wan et al. 2016 ; Sogabe et al. 2016 ; Hu et al. 2016 ). Other major research includes feature identification for long-term climate analysis ( Kurth

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

based on linear regression (e.g., Glahn and Lowry 1972 ), ML techniques are not necessarily linear. A variety of ML approaches, other than regression, have been applied to weather prediction since the 1980s and include: artificial neural networks (ANNs; e.g., Key et al. 1989 ; Marzban and Stumpf 1996 ; Kuligowski and Barros 1998 ; Hall et al. 1999 ; Manzato 2007 ; Rajendra et al. 2019 ), support vector machines (e.g., Ortiz-García et al. 2014 ; Adrianto et al. 2009 ), clustering algorithms

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