A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning
Open accessAbstract
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.
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Self-citationProspects for Machine Learning Activity within the United States National Weather Service
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Machine Learning Estimation of Maximum Vertical Velocity from Radar
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Assessing the Impact of Biased Target Variables on Machine Learning Models of Severe Hail
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- Authors
- Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., & McGovern, A.
- Affiliations
- University of Oklahoma (US)
- NOAA Storm Prediction Center (US)
- North Carolina State University (US)
- Venue
- Weather and Forecasting
- Publisher
- American Meteorological Society
- Published
- 2022-08
- Volume
- 37
- Issue
- 8
- Pages
- 1509-1529
- ISSN
- 0882-8156 (ISSN-L)
- 1520-0434
- OpenAlex
- W4224009465
- Cited by
- 25
- References
- 88
- Citations by year
- 2025: 6 · 2024: 10 · 2023: 8 · 2022: 1
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Weather and Forecasting (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2019758)
- U.S. Department of Commerce (NA21OAR4320204)
- University of Oklahoma (NA21OAR4320204, NA16OAR4320115)
- National Oceanic and Atmospheric Administration (NA16OAR4320115, #NA21OAR4320204)
- NOAA Research (NA21OAR4320204, NA16OAR4320115)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- operational meteorology, linear regression, logistic regression, decision tree, random forest, gradient boosting decision tree, naive Bayes, support vector machine, machine learning education, black box opacity, Jupyter notebooks, Google Colaboratory
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