# A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning

arXiv: https://arxiv.org/abs/2204.07492 · HTML: https://mlsanity.com/paper/2204.07492/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 110 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model](https://mlsanity.com/paper/1909.04711/) (2019)
- [Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations](https://mlsanity.com/paper/2004.07906/) (2020)
- [Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System](https://mlsanity.com/paper/2012.00679/) (2020)
- [Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting](https://mlsanity.com/paper/1711.10937/) (2017)

## Built on by (most cited first, 6 of 6)

- [A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data](https://mlsanity.com/paper/2401.16437/) (2024)
- [A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning](https://mlsanity.com/paper/2211.00147/) (2022)
- [Machine Learning Estimation of Maximum Vertical Velocity from Radar](https://mlsanity.com/paper/2310.09392/) (2023)
- [Long-term hail risk assessment with deep neural networks](https://mlsanity.com/paper/2209.01191/) (2022)
- [Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting](https://mlsanity.com/paper/2309.15207/) (2023)
- [Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction](https://mlsanity.com/paper/2508.07062/) (2025)

## Abstract (arXiv)

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 every day workflow. To reduce the opaqueness of machine learning methods and lower hesitancy towards 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; naive 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 catalyse the use of machine learning in meteorology.