2023 journal article
A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning
WEATHER AND FORECASTING, 38(8), 1271–1293.
author keywords: Radars; Radar observations; Satellite observations; Forecasting techniques; Nowcasting; Operational forecasting; Machine learning
TL;DR:
This paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community and discusses the terms associated with neural networks and their training.
(via Semantic Scholar)
open access via journals.ametsoc.org (publisher PDF)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
Source: Web Of Science
Added: September 5, 2023
Abstract 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. 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 to 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 is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I 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.