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Joaquin Cuomo and V. Chandrasekar

learning models for weather nowcasting using radar echo data and compared them with existing models, particularly against two operational models. A common issue with deep learning approaches is that they tend to underestimate the intensity of reflectivity. To address this, we proposed the Composite model, which shows promising results on enhancing the predictions at higher reflectivity values. Although this ensemble can improve any base model, we believe that the primary focus should be put on

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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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Ryan Lagerquist, David Turner, Imme Ebert-Uphoff, Jebb Stewart, and Venita Hagerty

our work unique. First, we use U-net++ models ( Zhou et al. 2020 ), as opposed to the fully connected networks [sometimes called “dense” or “feed-forward”; see chapter 6 of Goodfellow et al. (2016 )] used in previous work. U-net++ models are a type of deep learning, which can exploit spatial patterns in gridded data to make better predictions. Second, we have built physical constraints and vertical nonlocality into the U-net++ models, allowing them to handle nonadjacent cloud layers and better

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Jing-Yi Zhuo and Zhe-Min Tan

postprocessed guidance such as wind speed probabilities (e.g., DeMaria et al. 2013 ). Therefore, more accurate estimations of TC intensity and wind radii are still badly needed. Moreover, with the recent advances in the satellite observations of TCs, updating the analysis technique, especially objective algorithms that can interpret complex TC dynamics from the satellite observations, is of vital importance. Deep learning is a type of artificial intelligence algorithm that has revolutionized computer

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John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

thunderstorms into a single output, we utilize a deep-learning approach that mimics expert human pattern recognition of intense convection in satellite imagery. The goal of this approach is to quantify convective intensity automatically, saving forecasters time in identifying, diagnosing, and prioritizing threats. Deep learning is a branch of machine-learning methods based on artificial neural networks with feature learning, or the ability to automatically find salient features in data (e.g., Schmidhuber

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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

Open access
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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