2022 journal article
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning
WEATHER AND FORECASTING, 37(8), 1509–1529.
author keywords: Radars; Radar observations; Satellite observations; Forecasting techniques; Nowcasting; Operational forecasting; Artificial intelligence; Classification; Data science; Decision trees; Machine learning; Model interpretation and visualization; Regression; Support vector machines; Other artificial intelligence; machine learning
TL;DR:
A survey of some of the most common machine learning methods in meteorology, including linear regression; logistic regression; decision trees; random forest; gradient boosted decision Trees; naive Bayes; and support vector machines is provided.
(via Semantic Scholar)
open access via journals.ametsoc.org (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
13. Climate Action
(Web of Science)
Source: Web Of Science
Added: September 19, 2022
Abstract Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are “black boxes” and thus end-users are hesitant to apply the machine learning methods in their everyday workflow. To reduce the opaqueness of machine learning methods and lower hesitancy toward machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression, logistic regression, decision trees, random forest, gradient boosted decision trees, naïve Bayes, and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyze the use of machine learning in meteorology.