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This research aims to enhance severe weather predictions using random forest models trained on historical forecast data.

Forecasting Severe Weather with Random Forests

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This research aims to enhance severe weather predictions using random forest models trained on historical forecast data.
Trained random forest models on nine years of NOAA weather forecasts from 2003 to 2012.
Created separate models for tornado, hail, and severe wind predictions at Day 1.
Evaluated model performance against SPC outlooks using data from April 2012 to December 2016.
RF forecasts slightly underperform compared to SPC at Day 1 but significantly outperform at Days 2 and 3.
A blend of SPC and RF forecasts significantly improves prediction accuracy over SPC forecasts alone.

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Randomized trial compares severe weather predictions in the contiguous United States, indicating potential improvements in forecasting accuracy.

Hill et al. (2020) studied this question.

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Randomized trial compares severe weather predictions in the contiguous United States, indicating potential improvements in forecasting accuracy.