[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118742-en":3,"doc-seo-118742-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118742,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",6,"Technology","A Machine Learning Tutorial for Operational Meteorology, Part II - Neural Networks and Deep Learning","Machine learning in meteorology has expanded rapidly, with neural networks and deep learning adopted at an unprecedented rate. This second tutorial paper in a two-part pair provides a plain-language neural-network focused resource tailored to the operational meteorological community. It surveys perceptrons, artificial neural networks, convolutional neural networks, and U-Net style architectures, covering key terminology and training concepts while giving intuition for each approach. A meteorological case study diagnoses thunderstorms from satellite imagery, supported by an open-source code repository for exploration with the included dataset or templates for alternate datasets.","arXiv :2211 .00147v2 [ cs .LG] 12 Mar 2023  \nGenerated using the official AMS LATEX template v6.1 two-column layout. This work has been submitted for publication. Copyright in this work may be transferred without further notice, and this version may no longer be accessible.  \nA Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and  \nDeep Learning  \nRandy J. Chase a,b,c , David R. Harrisonb,d,e , Gary M. Lackmannf and Amy McGoverna,b,c  \na School of Computer Science, University of Oklahoma, Norman OK USA  \nb School of Meteorology, University of Oklahoma, Norman OK USA  \nc NSFAI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma, Norman OK  \nUSA  \nd Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman OK USA  \ne NOAA/NWS/Storm Prediction Center, Norman, Oklahoma  \nf Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina  \nABSTRACT: Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods are discussed. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks. Like the part 1 paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes) . This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.  \n1. Introduction  \nIn the previous part of this tutorial series Chase et al.(2022) (hereafter Part 1) provided a survey of many of the most common traditional machine learning techniques that a meteorologist might encounter. This included: linear regression, logistic regression, naive bayes, decision trees, random forest, gradient boosted trees and support vector machines. Beyond discussing the formulation of the methods, Part 1 also discussed the general terms associated with machine learning and provided an end-to-end machine learning example to detect lightning flashes within satellite and radar images. In this manuscript we continue our explanation and tutorial of supervised machine learning techniques by discussing a rapidly expanding category of machine learning known as neural networks and deep learning.  \nWhile neural networks can be viewed similarly to the other methods described in Part 1 (i.e., an empirical tool for making predictions and classifications), there are numerous nuances and different terms associated with neural networks that motivate their own detailed discussion. Furthermore, given the accelerated growth of neural networks (c.f., Fig. 1e in Part 1) and recent impressive demonstrations of neural networks achieving similar forecasting performance to numerical weather prediction (e.g., Weynet al. 2020; Rasp and Thuerey 2021; Ravuri et al. 2021;  \nCorresponding author: Randy J. Chase, [randy.chase@colostate.edu](randy.chase@colostate.edu)  \nEspeholt et al. 2022; Keisler 2022; Lam et al. 2022; Bi et al. 2022; Nguyen et al. 2023), the meteorological literature could benefit from a neu","cbCaij2wb3uhOW0y","https://ap.wps.com/l/cbCaij2wb3uhOW0y","pdf",5970214,1,23,"English","en",105,"# Introduction\n## Neural network methods and common terms\n## Applying neural networks to a meteorological example\n## Summary","[{\"question\":\"What is the purpose of Part II in the tutorial series?\",\"answer\":\"It serves as a neural-network specific, plain-language resource for operational meteorologists, continuing from Part I’s traditional machine learning overview.\"},{\"question\":\"Which neural network architectures are covered in the paper?\",\"answer\":\"The paper discusses perceptrons, artificial neural networks, convolutional neural networks, and U-Net style (“U” shaped) networks.\"},{\"question\":\"How do readers apply the methods for a meteorological task?\",\"answer\":\"The manuscript provides intuition and a meteorological example diagnosing thunderstorms from satellite images, and it is accompanied by an open-source code repository for experimentation.\"}]","A Machine Learning Tutorial for Operational Meteorology, Part II - 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