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Ricardo Martins Campos, Vladimir Krasnopolsky, Jose-Henrique G. M. Alves, and Stephen G. Penny

potential. Berbić et al. (2017) applied ANNs and support vector machines for short forecasts of significant wave height. Dixit and Londhe (2016) developed a neuro wavelet technique, combining discrete wavelet transform and ANNs, to explore the predictability of extreme events for five major hurricanes at four locations in the Gulf of Mexico. Deo et al. (2001) , Deo and Sridhar Naidu (1998) , and Mandal and Prabaharan (2006) developed ANN systems to predict significant wave heights in India, and

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

1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites

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

. Kingfield , K. Ortega , and T. Smith , 2019 : Estimates of gradients in radar moments using a linear least squares derivative technique . Wea. Forecasting , 34 , 415 – 434 , https://doi.org/10.1175/WAF-D-18-0095.1 . 10.1175/WAF-D-18-0095.1 Markowski , P. , and Y. Richardson , 2009 : Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research . Atmos. Res. , 93 , 3 – 10 , https://doi.org/10.1016/j.atmosres.2008.09.015 . 10.1016/j

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

, multiple-physics CAEs is an artificial inflation of ensemble spread due to the existence of systematic biases between ensemble members ( Eckel and Mass 2005 ; Clark et al. 2010b ; Loken et al. 2019 ). These shortcomings are typically resolved using one or more postprocessing techniques, including isotropic (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ; Roberts et al. 2019 ) or anisotropic (e.g., Marsh et al. 2012 ) spatial smoothing of the raw forecast probability field

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

the model resolution, to extend the forecast horizon, and to increase ensemble size). Flexibility. ML techniques can accommodate (i) variables that have not been (and sometimes cannot be) included in physically based models, (ii) physical constrains (like conservation laws or balance equations), (iii) processes that are nonlinear, (iv) non-Gaussian observation errors, and (v) empirical data for processes for which the true physics is poorly understood ( Krasnopolsky 2013 ). Ease of use. Modern

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

techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study

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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

observations and satellite applications, including the use of neural networks for NWP model parameterization ( Krasnopolsky et al. 2010 ) and using deep learning to infer missing data ( Boukabara et al. 2019a ). NCAR, a federally funded research and development center, has a long history of developing AI techniques for weather applications. Haupt et al. (2019) highlighted the Dynamic Integrated Forecast (DICast) system, a 20-year effort at NCAR that forms the “weather engine” of many applications, as

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

. Furthermore, a new generation of infrared imagers ( Schmit et al. 2005 , 2017 ) and emerging cubesat sensors ( Blackwell et al. 2012 ; Cahoy et al. 2015 ; Reising et al. 2016 ) require new, robust techniques for TC nowcasting and forecasting that can assimilate a spectrally diverse variety of images all at once. It is well known that the microwave imaging frequencies available on polar-orbiting meteorological satellites since the late 1980s offer unique perspectives for observing TCs. Uses of the

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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 , https://doi.org/10.3402/tellusa.v7i3.8902 . 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 , https://doi.org/10.1175/1520-0434(1997)012<0154:UORTTP>2.0.CO;2 . 10

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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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