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grid (about 200 km). On longer climate time scales (decades to centuries), Dagon et al. (2022) detected weather fronts (e.g., cold, warm, occluded, and stationary fronts) using a convolutional neural network and then quantified their association with extreme precipitation over North America along with projected changes in a future climate; thus, feature detection was used to extract precursors to extreme precipitation events. Skillful ML-based prediction of weather and climate has garnered broad
grid (about 200 km). On longer climate time scales (decades to centuries), Dagon et al. (2022) detected weather fronts (e.g., cold, warm, occluded, and stationary fronts) using a convolutional neural network and then quantified their association with extreme precipitation over North America along with projected changes in a future climate; thus, feature detection was used to extract precursors to extreme precipitation events. Skillful ML-based prediction of weather and climate has garnered broad
. Randall , 2019 : The seasonality and regionality of MJO impacts on North American temperature . Geophys. Res. Lett. , 46 , 9193 – 9202 , https://doi.org/10.1029/2019GL083950 . Johnson , N. C. , D. C. Collins , S. B. Feldstein , M. L. L’Heureux , and E. E. Riddle , 2014 : Skillful wintertime North American temperature forecasts out to 4 weeks based on the state of ENSO and the MJO . Wea. Forecasting , 29 , 23 – 38 , https://doi.org/10.1175/WAF-D-13-00102.1 . Klein , W. H
. Randall , 2019 : The seasonality and regionality of MJO impacts on North American temperature . Geophys. Res. Lett. , 46 , 9193 – 9202 , https://doi.org/10.1029/2019GL083950 . Johnson , N. C. , D. C. Collins , S. B. Feldstein , M. L. L’Heureux , and E. E. Riddle , 2014 : Skillful wintertime North American temperature forecasts out to 4 weeks based on the state of ENSO and the MJO . Wea. Forecasting , 29 , 23 – 38 , https://doi.org/10.1175/WAF-D-13-00102.1 . Klein , W. H
). Annual rainfall is commonly less than 750 mm ( Leal et al. 2005 ), and it is highly variable ( Moura and Shukla 1981 ). The region experiments a peculiar intra-annual rainfall regime, with a maximum in March–April over the north and the center part and in November–March over the southern part. Cerrado is a tropical savanna covering 22% of Brazil’s territory. The overall amount of rain is usually between 800 and 2000 mm yr −1 ( Ratter et al. 1997 ), mostly distributed between October and April
). Annual rainfall is commonly less than 750 mm ( Leal et al. 2005 ), and it is highly variable ( Moura and Shukla 1981 ). The region experiments a peculiar intra-annual rainfall regime, with a maximum in March–April over the north and the center part and in November–March over the southern part. Cerrado is a tropical savanna covering 22% of Brazil’s territory. The overall amount of rain is usually between 800 and 2000 mm yr −1 ( Ratter et al. 1997 ), mostly distributed between October and April
–2030 mm), and mostly as snow above around 6000 ft (1800 m). However, most regions eastward of the crest are in a rain shadow, annually receiving less than about 25 in. (635 mm). Summers are dry, with afternoon thunderstorms occurring mostly from North American monsoon in mid- and late summer. This makes Nevada the driest state in the United States. The progression of biotic zones along increasing elevation in the Sierra Nevada is the western foothills of grassland/savanna/woodland, Pinyon pine
–2030 mm), and mostly as snow above around 6000 ft (1800 m). However, most regions eastward of the crest are in a rain shadow, annually receiving less than about 25 in. (635 mm). Summers are dry, with afternoon thunderstorms occurring mostly from North American monsoon in mid- and late summer. This makes Nevada the driest state in the United States. The progression of biotic zones along increasing elevation in the Sierra Nevada is the western foothills of grassland/savanna/woodland, Pinyon pine
SREF analyses herein are output to a grid with 32-km horizontal grid spacing (NCEP grid 221). SREF configuration details are summarized in Table 2 . Table 2. SREF member specifications, adapted from Du et al. (2015) . Initial conditions (ICs) are taken from the operational Rapid Refresh (RAP; Benjamin et al. 2016 ), the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS), and the North American Mesoscale Model Data Assimilation System (NDAS). IC perturbations
SREF analyses herein are output to a grid with 32-km horizontal grid spacing (NCEP grid 221). SREF configuration details are summarized in Table 2 . Table 2. SREF member specifications, adapted from Du et al. (2015) . Initial conditions (ICs) are taken from the operational Rapid Refresh (RAP; Benjamin et al. 2016 ), the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS), and the North American Mesoscale Model Data Assimilation System (NDAS). IC perturbations
spatial domain for each day is different and generally not CONUS-wide, but the domain usually covers a large portion of the CONUS, including most tornadoes on the given day and many nontornadic storms. At each 5-min time step, GridRad includes four variables on the 3D grid: reflectivity, spectrum width, vorticity (twice azimuthal shear), and divergence (twice radial shear). The RAP is a nonhydrostatic mesoscale model with 13- or 20-km grid spacing and covers much of North America, including the full
spatial domain for each day is different and generally not CONUS-wide, but the domain usually covers a large portion of the CONUS, including most tornadoes on the given day and many nontornadic storms. At each 5-min time step, GridRad includes four variables on the 3D grid: reflectivity, spectrum width, vorticity (twice azimuthal shear), and divergence (twice radial shear). The RAP is a nonhydrostatic mesoscale model with 13- or 20-km grid spacing and covers much of North America, including the full
, e0130140, https://doi.org/10.1371/journal.pone.0130140 . 10.1371/journal.pone.0130140 Bang , S. D. , and E. J. Zipser , 2015 : Differences in size spectra of electrified storms over land and ocean . Geophys. Res. Lett. , 42 , 6844 – 6851 , https://doi.org/10.1002/2015GL065264 . 10.1002/2015GL065264 Benjamin , S. G. , and Coauthors , 2016 : A North American hourly assimilation and model forecast cycle: The Rapid Refresh . Mon. Wea. Rev. , 144 , 1669 – 1694 , https://doi.org/10
, e0130140, https://doi.org/10.1371/journal.pone.0130140 . 10.1371/journal.pone.0130140 Bang , S. D. , and E. J. Zipser , 2015 : Differences in size spectra of electrified storms over land and ocean . Geophys. Res. Lett. , 42 , 6844 – 6851 , https://doi.org/10.1002/2015GL065264 . 10.1002/2015GL065264 Benjamin , S. G. , and Coauthors , 2016 : A North American hourly assimilation and model forecast cycle: The Rapid Refresh . Mon. Wea. Rev. , 144 , 1669 – 1694 , https://doi.org/10
). USGS Techniques and Methods Doc. 6-B9, 38 pp. , https://doi.org/10.3133/tm6B9 . 10.3133/tm6B9 Thornton , P. E. , M. M. Thornton , B. W. Mayer , N. Wilhelmi , Y. Wei , R. Devarakonda , and R. B. Cook , 2014 : Daymet: Daily surface weather data on a 1-km grid for North America, version 2. ORNL DAAC, accessed 15 December 2020 , https://doi.org/10.3334/ORNLDAAC/1219 . 10.3334/ORNLDAAC/1219 Tian , Y. , Y.-P. Xu , Z. Yang , G. Wang , and Q. Zhu , 2018 : Integration
). USGS Techniques and Methods Doc. 6-B9, 38 pp. , https://doi.org/10.3133/tm6B9 . 10.3133/tm6B9 Thornton , P. E. , M. M. Thornton , B. W. Mayer , N. Wilhelmi , Y. Wei , R. Devarakonda , and R. B. Cook , 2014 : Daymet: Daily surface weather data on a 1-km grid for North America, version 2. ORNL DAAC, accessed 15 December 2020 , https://doi.org/10.3334/ORNLDAAC/1219 . 10.3334/ORNLDAAC/1219 Tian , Y. , Y.-P. Xu , Z. Yang , G. Wang , and Q. Zhu , 2018 : Integration
, Canada, Neural Information Processing Systems Foundation. Benjamin , S. , and Coauthors , 2004 : An hourly assimilation–forecast cycle: The RUC . Mon. Wea. Rev. , 132 , 495 – 518 , https://doi.org/10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2 . 10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2 Benjamin , S. , and Coauthors , 2016 : A North American hourly assimilation and model forecast cycle: The Rapid Refresh . Mon. Wea. Rev. , 144 , 1669 – 1694 , https://doi.org/10.1175/MWR-D-15
, Canada, Neural Information Processing Systems Foundation. Benjamin , S. , and Coauthors , 2004 : An hourly assimilation–forecast cycle: The RUC . Mon. Wea. Rev. , 132 , 495 – 518 , https://doi.org/10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2 . 10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2 Benjamin , S. , and Coauthors , 2016 : A North American hourly assimilation and model forecast cycle: The Rapid Refresh . Mon. Wea. Rev. , 144 , 1669 – 1694 , https://doi.org/10.1175/MWR-D-15
