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contributor authorWallace, Robinson
contributor authorFriedrich, Katja
contributor authorKalina, Evan A.
contributor authorSchlatter, Paul
date accessioned2019-09-22T09:02:51Z
date available2019-09-22T09:02:51Z
date copyright11/28/2018 12:00:00 AM
date issued2018
identifier otherWAF-D-18-0053.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4262479
description abstractThunderstorms that produce surface hail accumulations, sometimes as large as 60 cm in depth, have significantly affected the residents of the Front Range and High Plains of Colorado and Wyoming by creating hazardous road conditions and endangering lives and property. To date, surface hail accumulation is not part of a routine forecasting or monitoring system. Extensive coordinated hail accumulation reports and operational products designed to identify deep hail accumulating storms in real time are lacking. Kalina et al. used dual-polarization WSR-88D radar observations to calculate hail depth and hail accumulations but never validated the algorithm. This study shows how 20 quality-controlled hail depth reports from the hail depth database built by the Colorado Hail Accumulation from Thunderstorms (CHAT) project are being used to validate the Kalina et al. radar-based hail accumulation algorithm for operational application. The validated algorithm shows increased correlations between radar-derived and reported accumulations for hail depth reports not included in the validation. Furthermore, increases in computational efficiency have allowed the improved algorithm to be used operationally. With an improved hail accumulation algorithm, thunderstorms that produce hail accumulations are more frequently detected than previously reported.
publisherAmerican Meteorological Society
titleUsing Operational Radar to Identify Deep Hail Accumulations from Thunderstorms
typeJournal Paper
journal volume34
journal issue1
journal titleWeather and Forecasting
identifier doi10.1175/WAF-D-18-0053.1
journal fristpage133
journal lastpage150
treeWeather and Forecasting:;2018:;volume 034:;issue 001
contenttypeFulltext


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