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Dan Lu, Goutam Konapala, Scott L. Painter, Shih-Chieh Kao, and Sudershan Gangrade

, Le et al. (2019) applied LSTM for flood forecasting based on 24 years of daily data with 18 years of data for training, 5 years for validation, and the remaining 2 years for testing. Tian et al. (2018) trained the LSTM models with 10 years of daily data and validated streamflow simulation at two river basins using another 5 years. Kratzert et al. (2019a) considered 30 years of daily data and trained LSTM on 15 years and tested the performance on the remaining 15 years. Regardless of the

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

. Atlantic Forest is the second-largest rain forest of the American continent and one of the world’s regions hosting the biggest biodiversity. Annual rainfall is between 1000 and 3000 mm. The Brazilian Pampa represents 2.07% of the national territory and lies within the South Temperate Zone ( Roesch et al. 2009 ). The annual precipitation in the region is around 1200–1600 mm. The Pantanal wetland is a complex of seasonally inundated floodplains along the upper Paraguay River, located mostly in Brazil

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

-020-10234-7 He , J. , X. Bian , Y. Fu , and Y. Qin , 2012 : Research on water consumption and its law of main crops in west Liaohe River plain . Jieshui Guan’gai , 11 , 1 – 4 . Hirschi , M. , and Coauthors , 2011 : Observational evidence for soil-moisture impact on hot extremes in southeastern Europe . Nat. Geosci. , 4 , 17 – 21 , . 10.1038/ngeo1032 Huang , P. M. , Y. Li , and M. E. Sumner , 2011 : Handbook of Soil Sciences: Properties and

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

, the northern Great Plains, and the Ohio River valley—would greatly improve performance there. Fig . 8. Regional performance of the GridRad model on testing data: (a) number of examples, (b) number of tornadic examples (or “events”), (c) AUC, (d) CSI, (e) POD, and (f) FAR. Each grid cell is 100 km × 100 km. Fig . 9. As in Fig. 8 , but for MYRORSS. Figures 10 – 13 show extreme cases: the 100 best hits, worst false alarms, worst misses, and best correct nulls in the testing data ( Table 7 ). These

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