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. 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
. 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
-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 , https://doi.org/10.1038/ngeo1032 . 10.1038/ngeo1032 Huang , P. M. , Y. Li , and M. E. Sumner , 2011 : Handbook of Soil Sciences: Properties and
-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 , https://doi.org/10.1038/ngeo1032 . 10.1038/ngeo1032 Huang , P. M. , Y. Li , and M. E. Sumner , 2011 : Handbook of Soil Sciences: Properties and
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
, 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
, 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
. 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
