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.
Weather and Forecasting – American Meteorological Society
Published: Nov 21, 2017
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