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  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
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Amy McGovern, Ryan Lagerquist, David John Gagne II, G. Eli Jergensen, Kimberly L. Elmore, Cameron R. Homeyer, and Travis Smith

extreme weather patterns such as tropical cyclones, atmospheric rivers, and synoptic-scale fronts ( Liu et al. 2016 ; Mahesh et al. 2018 ; Kunkel et al. 2018 ; Lagerquist et al. 2019b ). The authors have extensive experience using ML to improve forecasting and understanding of weather phenomena ( Gagne et al. 2017a , b ; Lagerquist et al. 2017 ; McGovern et al. 2017; Gagne et al. 2019 ; Lagerquist et al. 2018 ). Many of these products have been used by human meteorologists in experiments and day

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Imme Ebert-Uphoff and Kyle Hilburn

. Pattern complexity is very difficult to evaluate for several reasons: 1) patterns can only be evaluated after NN training is completed; 2) techniques for discovering patterns, such as feature visualization ( Olah et al. 2017 , 2018 ), to date only provide limited answers; and 3) feature visualization is even more challenging for meteorological imagery, because it tends to have amorphous boundaries (e.g., clouds, atmospheric rivers, ocean eddies) ( Karpatne et al. 2019 ) rather than the crisp

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