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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
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
. 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
. 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
