Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

Lagerquist, Ryan; McGovern, Amy; Smith, Travis

doi:10.1175/waf-d-17-0038.1

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Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

2017
By Lagerquist, Ryan ; McGovern, Amy ; Smith, Travis
Source: Weather and Forecasting, 32(6), 2175-2193

[PDF-2.97 MB]

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Journal Title:

Weather and Forecasting
Personal Author:

Lagerquist, Ryan ; McGovern, Amy ; Smith, Travis

Lagerquist, Ryan ; McGovern, Amy ; Smith, Travis Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) Less -
Description:

Thunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) 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 United States, 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 receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the 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 during the 2017 Hazardous Weather Testbed.
Keywords:

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Convection Machine Learning Severe Storms Wind
Source:

Weather and Forecasting, 32(6), 2175-2193
DOI:

https://doi.org/10.1175/waf-d-17-0038.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072
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urn:sha256:4c67d0166af3a7c6db3757d31b38caaa2b536e4172cb843f5e042b47833fdcff
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https://repository.library.noaa.gov/view/noaa/48311/noaa_48311_DS1.pdf
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Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

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