Radar-Based Comparison of Thunderstorm Outflow Boundary Speeds versus Peak Wind Gusts from Automated Stations
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Radar-Based Comparison of Thunderstorm Outflow Boundary Speeds versus Peak Wind Gusts from Automated Stations

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  • Journal Title:
    Weather and Forecasting
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  • Description:
    Straight-line winds are arguably the most challenging element considered by operational forecasters when issuing severe thunderstorm warnings. Determining the potential maximum surface wind gust prior to an observed, measured gust is very difficult. This work builds upon prior research that quantified a relationship between the observed outflow boundary speed and corresponding measured wind gusts. Though this prior study was limited to a 30-case dataset over eastern Colorado, the current study comprises 943 cases across the contiguous United States and encompasses all times of day, seasons, and regions while representing various convective modes and associated near-storm environments.The wind gust ratios (WGRs), or the ratio between a measured wind gust and the associated outflow boundary speed, had a nationwide median of 1.44, mean of 1.68, and 25th–75th percentiles of 1.19–1.91, respectively. WGRs varied considerably by region, season, time of day, convective mode, near-storm environment, and outflow boundary speed. WGRs tended to be higher in the plains, Intermountain West, and southern coastal regions, lower in the cool season and during the morning and overnight, and lower in linear convective modes compared to supercell and disorganized modes. Environments with stronger mean winds and low-to-midlevel shear vector magnitudes tended to have lower WGRs, while those with steeper low-level lapse rates and other thermodynamic characteristics favorable for momentum transfer and evaporative cooling tended to have higher WGRs. As outflow boundary speed increases, WGRs—and their variability—decreases. Applying these findings may help operational meteorologists provide more accurate severe thunderstorm warnings.
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  • Source:
    Weather and Forecasting (2021)
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  • ISSN:
    0882-8156;1520-0434;
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