A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning

• URL: http://arxiv.org/abs/2211.00147v1
• Date: Mon, 31 Oct 2022 21:10:48 GMT
• Title: A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning
• Authors: Randy J. Chase, David R. Harrison, Gary Lackmann and Amy McGovern
• Abstract summary: This paper discusses machine learning methods in a plain language format that is targeted for the operational meteorolgical community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been being 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 meteorolgical 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 by 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.

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