Machine Learning for Real-time Prediction of Damaging Straight-line Convective Wind

Machine Learning for Real-time Prediction of Damaging Straight-line Convective Wind AbstractThunderstorms in the U.S. cause over 100 deaths and $10 billion of damage per year, much of which is attributable to straight-line (non-tornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥ 50 kt or 25.7 m s−1) for each storm cell in the continental U.S., at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the near-storm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the ROC curve ranges from 0.88 to 0.95; critical success index (CSI) ranges from 0.27 to 0.91; and Brier skill score (BSS) ranges from 0.19 to 0.65 (> 0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0-15 min), while the worst value occurs for the greatest distance (5-10 km outside storm cell) and/or lead time (60-90 min). The system was deployed in the 2017 Hazardous Weather Testbed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Machine Learning for Real-time Prediction of Damaging Straight-line Convective Wind

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0038.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThunderstorms in the U.S. cause over 100 deaths and $10 billion of damage per year, much of which is attributable to straight-line (non-tornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥ 50 kt or 25.7 m s−1) for each storm cell in the continental U.S., at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the near-storm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the ROC curve ranges from 0.88 to 0.95; critical success index (CSI) ranges from 0.27 to 0.91; and Brier skill score (BSS) ranges from 0.19 to 0.65 (> 0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0-15 min), while the worst value occurs for the greatest distance (5-10 km outside storm cell) and/or lead time (60-90 min). The system was deployed in the 2017 Hazardous Weather Testbed.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Nov 21, 2017

References

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