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

-scale Warn-on-Forecast system: A vision for 2020 . Bull. Amer. Meteor. Soc. , 90 , 1487 – 1500 , https://doi.org/10.1175/2009BAMS2795.1 . 10.1175/2009BAMS2795.1 Stensrud , D. , and Coauthors , 2013 : Progress and challenges with Warn-on-Forecast . Atmos. Res. , 123 , 2 – 16 , https://doi.org/10.1016/j.atmosres.2012.04.004 . 10.1016/j.atmosres.2012.04.004 Storm Prediction Center , 2020 : Mesoscale analysis pages. NOAA, accessed 9 March 2020 , http

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

://www.ncdc.noaa.gov/stormevents/ . Purdom , J. , 1976 : Some uses of high-resolution GOES imagery in mesoscale forecasting of convection and its behavior . Mon. Wea. Rev. , 104 , 1474 – 1483 , https://doi.org/10.1175/1520-0493(1976)104<1474:SUOHRG>2.0.CO;2 . 10.1175/1520-0493(1976)104<1474:SUOHRG>2.0.CO;2 Ronneberger , O. , P. Fischer , and T. Brox , 2015 : U-Net: Convolutional networks for biomedical image segmentation. MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention , N. Navab et al., Eds

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

1. Introduction Tropical cyclones (TCs) can cause numerous societal hazards, including storm surge, high winds, and flooding from excessive rainfall. Although these hazards are primarily associated with coastal locations, the strongest TCs often produce significant impacts far inland from the coast, particularly with regard to flooding. Prior knowledge of TC intensity is essential for resource deployment ahead of a landfalling system, yet TC intensity forecasts remain challenging due to the

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

and retrievals into the Warn-on-Forecast system . Mon. Wea. Rev. , 148 , 1829 – 1859 , https://doi.org/10.1175/MWR-D-19-0379.1 . 10.1175/MWR-D-19-0379.1 Kong , R. , M. Xue , A. O. Fierro , Y. Jung , C. Liu , E. R. Mansell , and D. R. MacGorman , 2020 : Assimilation of GOES-R Geostationary Lightning Mapper flash extent density data in GSI EnKF for the analysis and short-term forecast of a mesoscale convective system . Mon. Wea. Rev. , 148 , 2111 – 2133 , https

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

forecasting over the globe (e.g., Hamill 2012 ; Su et al. 2014 ; He et al. 2010 ; Cloke and Pappenberger 2009 ). However, few studies have focused on assessing the NWP precipitation predictability associated with large and intense mesoscale convective systems ( Bechtold et al. 2012 ), such as tropical rainfall. Atmospheric convection is an essential process for understanding and modeling the weather dynamics over the tropics ( Bony et al. 2015 ), but one very difficult to analytically represent in

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

SREF analyses herein are output to a grid with 32-km horizontal grid spacing (NCEP grid 221). SREF configuration details are summarized in Table 2 . Table 2. SREF member specifications, adapted from Du et al. (2015) . Initial conditions (ICs) are taken from the operational Rapid Refresh (RAP; Benjamin et al. 2016 ), the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS), and the North American Mesoscale Model Data Assimilation System (NDAS). IC perturbations

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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

– 706 , https://doi.org/10.1002/qj.392 . 10.1002/qj.392 Madaus , L. E. , and C. F. Mass , 2017 : Evaluating smartphone pressure observations for mesoscale analyses and forecasts . Wea. Forecasting , 32 , 511 – 531 , https://doi.org/10.1175/WAF-D-16-0135.1 . 10.1175/WAF-D-16-0135.1 McCandless , T. C. , G. S. Young , S. Haupt , and L. M. Hinkelman , 2016 : Regime-dependent short-range solar irradiance forecasting . J. Appl. Meteor. Climatol. , 55 , 1599 – 1613 , https

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

.12.045008 Madaus , L. E. , and C. F. Mass , 2017 : Evaluating smartphone pressure observations for mesoscale analyses and forecasts . Wea. Forecasting , 32 , 511 – 531 , https://doi.org/10.1175/WAF-D-16-0135.1 . 10.1175/WAF-D-16-0135.1 Manogaran , G. , V. Vijayakumar , R. Varatharajan , P. M. Kumar , R. Sundarasekar , and C.-H. Hsu , 2018 : Machine learning based Big Data processing framework for cancer diagnosis using hidden Markov model and GM clustering . Wireless

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

://doi.org/10.1109/78.650102 . 10.1109/78.650102 Schwartz , C. , G. Romine , M. Weisman , R. Sobash , K. Fossell , K. Manning , and S. Trier , 2015 : A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses . Wea. Forecasting , 30 , 1158 – 1181 , https://doi.org/10.1175/WAF-D-15-0013.1 . 10.1175/WAF-D-15-0013.1 Selvaraju , R. , M. Cogswell , A. Das , R. Vedantam , D. Parikh , and D. Batra , 2017 : Grad

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Imme Ebert-Uphoff and Kyle Hilburn

. In contrast the image-to-image translation models ( Figs. 1b,c ) generate as output an image, typically of the same dimension (but not necessarily the same number of channels) as the input image. Image-to-image translation models can be used to enhance remote sensing images ( Tsagkatakis et al. 2019 ), to detect changes in satellite imagery ( Peng et al. 2019 ), for precipitation forecasting ( Sønderby et al. 2020 ), for weather forecasting ( Weyn et al. 2020 ), to detect tropical and

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