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Kanghui Zhou, Yongguang Zheng, Wansheng Dong, and Tingbo Wang

a good ability to combine the multisource data, and over 99% of the 550 observed initiation of MCS events were detected within 50 km. Deep learning (DL) is a subset of machine learning algorithms that uses multilayer artificial neural networks to deliver state-of-the-art accuracy in many tasks ( Bengio 2009 ; Schmidhuber 2015 ). Similar to traditional machine learning algorithms like artificial neural networks and SVM, DL networks can model complex nonlinear systems. Moreover, these networks

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

( Cintineo et al. 2018 ). Convolutional neural networks (CNN) are specially designed to learn from spatial grids and often contain many layers, which qualifies them as a deep-learning method (section 1.1.4 of Chollet 2018 ). In traditional ML, spatial grids must be transformed into scalar features, which become the direct inputs to the model. Examples are principal components, spatial statistics (such as means and standard deviations), and raw gridpoint values (where each value in the grid is treated as

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Wenwei Xu, Karthik Balaguru, Andrew August, Nicholas Lalo, Nathan Hodas, Mark DeMaria, and David Judi

, air–sea interaction and other processes in the TC core are highly nonlinear, which suggests that nonlinear methods have the potential to improve statistically based intensity forecast models. In particular, deep learning (DL), or deep neural networks, are capable of learning highly complex and nonlinear relationships involving many predictors. DL has achieved remarkable success in computer vision and pattern recognition in recent years. Yet its application to TC intensity prediction has been rare

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Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

where reference stations are sparse. We have shown that CNN-based classifiers can effectively use spatial patterns from both precipitation and elevation gridded inputs for station observation QC, and the classification result is largely improved over spatially agnostic models like MLP and decision trees. We think this finding further implies the potential of deep-learning-based QC as an alternative to other automated QC methods for handling more diverse input data forms. b. Notes on data skewness

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Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

. Second, these SD methods do not incorporate elevation as a predictor and may generate less accurate precipitation patterns in complex terrain. Convolutional neural networks (CNNs) are types of deep-learning methods that can learn nonlinear relationships with low generalization error across domains ( Zhang et al. 2017 ), and thus have the potential to overcome the limitation of traditional SD methods. In the first part of this research ( Sha et al. 2020 , hereinafter Part I ), CNNs with UNet

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Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

what operational meteorological centers can currently offer. Statistical downscaling (SD), a postprocessing technique that can generate localized meteorological information conditioned on coarse numerical model outputs or reanalysis data, has the potential to resolve this challenge and, thus, has received attention since the 1990s ( Wilby and Wigley 1997 ; Wilby et al. 1999 ; Dibike and Coulibaly 2005 ; Glotter et al. 2014 ). Convolutional neural networks (CNNs) are types of deep-learning models

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Jonny Mooneyham, Sean C. Crosby, Nirnimesh Kumar, and Brian Hutchinson

approach presented here. Here, we develop a deep learning model that we call Spectral Wave Residual Learning Network (SWRL Net) to improve numerical model predictions with directional wave buoy observations. Spectral wave predictions at buoy locations are used with collocated directional buoy observations to generate forecast corrections up to 24 h in the future. Frequency-directional spectra are transformed into the observed buoy moments resulting in a large feature set and large number of model

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David John Gagne II, Sue Ellen Haupt, Douglas W. Nychka, and Gregory Thompson

localized variations in temperature and moisture. HAILCAST has demonstrated skill in diagnosing hail size from proximity soundings ( Jewell and Brimelow 2009 ) and convection-allowing model environments ( Adams-Selin et al. 2019 ). In this paper, we demonstrate that incorporating both vertical profile and spatial information into a deep learning hail size diagnostic model can provide both increased hail size analysis skill and insight into important factors for hail growth. The importance of storm

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Yumeng Tao, Xiaogang Gao, Alexander Ihler, Soroosh Sorooshian, and Kuolin Hsu

extracting useful information in the satellite imageries and linking it to precipitation estimates ( Nasrollahi et al. 2013 ; Sorooshian et al. 2011 ). In recent years, deep learning techniques, also known as deep neural networks, have been developed and widely applied in the machine learning and computer vision areas ( Bengio 2009 ; Hinton et al. 2006 ; Vincent et al. 2010 ). Tao et al. (2016a , b) applied the methods to satellite-based precipitation estimation and demonstrated their effectiveness

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Ryan Lagerquist, Amy McGovern, and David John Gagne II

induced by wind shear and convergence between two anticyclones), the thermal method is better at detecting warm fronts (which are almost never detected by the wind-shift method). Machine learning (ML) is a process whereby computers learn autonomously from data, as opposed to an expert system like NFA, which is based on human-derived rules. Deep learning (DL) is a subset of ML, which offers the ability to encode the input data at various levels of abstraction. These abstractions are called features

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