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Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networkssupport with coding and parallelization, the atmospheric ensemble team at NCEP, and the observations provided by NDBC and NESDIS. REFERENCES Alves , J.-H. G. M. , and I. R. Young , 2003 : On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data . App. Ocean Res. , 25 , 167 – 186 , https://doi.org/10.1016/j.apor.2004.01.002 . 10.1016/j.apor.2004.01.002 Alves , J.-H. G. M. , and Coauthors , 2013 : The NCEP–FNMOC combined wave ensemble product
support with coding and parallelization, the atmospheric ensemble team at NCEP, and the observations provided by NDBC and NESDIS. REFERENCES Alves , J.-H. G. M. , and I. R. Young , 2003 : On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data . App. Ocean Res. , 25 , 167 – 186 , https://doi.org/10.1016/j.apor.2004.01.002 . 10.1016/j.apor.2004.01.002 Alves , J.-H. G. M. , and Coauthors , 2013 : The NCEP–FNMOC combined wave ensemble product
between humidity/temperature and heating/moistening, or likewise by automatic differentiation of nonlinear regression models ( Brenowitz and Bretherton 2019 ). Visualizing the LRF as a matrix does not predict the consequences of coupling the scheme to atmospheric fluid mechanics (i.e., the GCM’s “dynamical core”). Kuang (2010) takes this additional step by coupling CRM-derived LRFs with linearized gravity wave dynamics and further developing a vertically truncated ordinary differential equation
between humidity/temperature and heating/moistening, or likewise by automatic differentiation of nonlinear regression models ( Brenowitz and Bretherton 2019 ). Visualizing the LRF as a matrix does not predict the consequences of coupling the scheme to atmospheric fluid mechanics (i.e., the GCM’s “dynamical core”). Kuang (2010) takes this additional step by coupling CRM-derived LRFs with linearized gravity wave dynamics and further developing a vertically truncated ordinary differential equation
, R. M. , V. Krasnopolsky , J.-H. G. M. Alves , and S. G. Penny , 2019 : Nonlinear wave ensemble averaging in the Gulf of Mexico using neural network . J. Atmos. Oceanic Technol. , 36 , 113 – 127 , https://doi.org/10.1175/JTECH-D-18-0099.1 . 10.1175/JTECH-D-18-0099.1 Chang , B. , L. Meng , E. Haber , F. Tung , and D. Begert , 2018 : Multi-level residual networks from dynamical systems view . arXiv, 14 pp., https://arxiv.org/abs/1710.10348 . Chen , C.-C. , A
, R. M. , V. Krasnopolsky , J.-H. G. M. Alves , and S. G. Penny , 2019 : Nonlinear wave ensemble averaging in the Gulf of Mexico using neural network . J. Atmos. Oceanic Technol. , 36 , 113 – 127 , https://doi.org/10.1175/JTECH-D-18-0099.1 . 10.1175/JTECH-D-18-0099.1 Chang , B. , L. Meng , E. Haber , F. Tung , and D. Begert , 2018 : Multi-level residual networks from dynamical systems view . arXiv, 14 pp., https://arxiv.org/abs/1710.10348 . Chen , C.-C. , A
. Bevilacqua , 2018 : Computer vision and deep learning techniques for pedestrian detection and tracking: A survey . Neurocomputing , 300 , 17 – 33 , https://doi.org/10.1016/j.neucom.2018.01.092 . 10.1016/j.neucom.2018.01.092 Campos , R. M. , V. Krasnopolsky , J.-H. G. M. Alves , and S. G. Penny , 2019 : Nonlinear wave ensemble averaging in the Gulf of Mexico using neural network . J. Atmos. Oceanic Technol. , 36 , 113 – 127 , https://doi.org/10.1175/JTECH-D-18-0099.1 . 10.1175/JTECH
. Bevilacqua , 2018 : Computer vision and deep learning techniques for pedestrian detection and tracking: A survey . Neurocomputing , 300 , 17 – 33 , https://doi.org/10.1016/j.neucom.2018.01.092 . 10.1016/j.neucom.2018.01.092 Campos , R. M. , V. Krasnopolsky , J.-H. G. M. Alves , and S. G. Penny , 2019 : Nonlinear wave ensemble averaging in the Gulf of Mexico using neural network . J. Atmos. Oceanic Technol. , 36 , 113 – 127 , https://doi.org/10.1175/JTECH-D-18-0099.1 . 10.1175/JTECH
supercell thunderstorms ( Apke et al. 2016 ). Textural patterns at cloud top have also been used to infer updraft strength ( Bedka and Khlopenkov 2016 ). In the presence of strong upper-level flow, some overshoots generate above-anvil cirrus plumes (AACP) downstream from the overshooting top as a result of internal gravity wave breaking and are apparent in visible satellite imagery ( Wang 2003 ; Wang et al. 2016 ; Homeyer et al. 2017 ; Bedka et al. 2018 ). AACPs in visible imagery are responsible for
supercell thunderstorms ( Apke et al. 2016 ). Textural patterns at cloud top have also been used to infer updraft strength ( Bedka and Khlopenkov 2016 ). In the presence of strong upper-level flow, some overshoots generate above-anvil cirrus plumes (AACP) downstream from the overshooting top as a result of internal gravity wave breaking and are apparent in visible satellite imagery ( Wang 2003 ; Wang et al. 2016 ; Homeyer et al. 2017 ; Bedka et al. 2018 ). AACPs in visible imagery are responsible for
techniques in Earth science ( Reichstein et al. 2019 ). By predicting temporally varying target variables in land, ocean and atmosphere domains from temporally varying features, machine learning has been actively used to study Earth system dynamics. Particularly, compared to previous mechanistic or semiempirical modeling approaches, machine learning methods have been proven to be more powerful and flexible when inferring continental or global estimates from point observations, such as predicting carbon
techniques in Earth science ( Reichstein et al. 2019 ). By predicting temporally varying target variables in land, ocean and atmosphere domains from temporally varying features, machine learning has been actively used to study Earth system dynamics. Particularly, compared to previous mechanistic or semiempirical modeling approaches, machine learning methods have been proven to be more powerful and flexible when inferring continental or global estimates from point observations, such as predicting carbon
