The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

Sandmæl, Thea N.; Smith, Brandon R.; Reinhart, Anthony E.; Schick, Isaiah M.; Ake, Marcus C.; Madden, Jonathan G.; Steeves, Rebecca B.; Williams, Skylar S.; Elmore, Kimberly L.; Meyer, Tiffany C.

doi:10.1175/WAF-D-22-0123.1

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The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

2023
By Sandmæl, Thea N. ; Smith, Brandon R. ; Reinhart, Anthony E. ; ...
Source: Weather and Forecasting (published online ahead of print 2023)

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Journal Title:

Weather and Forecasting
Personal Author:

Sandmæl, Thea N. ; Smith, Brandon R. ; Reinhart, Anthony E. ; Schick, Isaiah M. ; Ake, Marcus C. ; ... More +

Sandmæl, Thea N. ; Smith, Brandon R. ; Reinhart, Anthony E. ; Schick, Isaiah M. ; Ake, Marcus C. ; Madden, Jonathan G. ; Steeves, Rebecca B. ; Williams, Skylar S. ; Elmore, Kimberly L. ; Meyer, Tiffany C. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations)

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) Less -
Description:

A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The Tornado Probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166,145 data points derived from 0.5°-tilt radar data and storm reports from 2011-2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and non-tornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision making. It has the ability to condense a multitude of radar data into a concise object-based information read-out that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.
Keywords:

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Machine Learning Tornadoes Tornado Warning Systems Atmospheric Science
Source:

Weather and Forecasting (published online ahead of print 2023)
DOI:

https://doi.org/10.1175/WAF-D-22-0123.1
Document Type:

Journal Article
Funding:

Grant no. NA21OAR4320204
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Accepted Manuscript
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Submitted
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urn:sha256:0258225d4683ed05d1e849fa0be168ac033ee6f8789e6be1c4d6a415f3579a3a
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https://repository.library.noaa.gov/view/noaa/48189/noaa_48189_DS1.pdf
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The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

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