S100 Debiasing Supercell Thunderstorm Density Distribution in the Great Plains

Sunday, 7 January 2018
Exhibit Hall 5 (ACC) (Austin, Texas)
Madeline R. Diedrichsen, University of Nebraska-Lincoln, Lincoln, NE; and A. Houston, C. Oppermann, and A. Torres

Supercell thunderstorm density clusters have been identified through a three-year supercell climatology of the US Great Plains from 2005 to 2007 developed using the ThOR algorithm, which is principally based on the radar data from the NEXRAD network. The supercell density distribution needs to be debiased and verified because of a range-dependent bias in mesocyclone detection. This study will debias the supercell thunderstorm density clusters identified near NEXRAD radar locations. The dataset will be obtained from an algorithm that combines the Thunderstorm Observation by Radar (ThOR) algorithm and mesocyclone Detection algorithm (MDA). The ThOR algorithm identifies thunderstorm tracks using NLDN data, level 2 radar data, and NARR data and the MDA identifies mesocyclones in thunderstorms by radar radial velocity data. Another algorithm will then be applied to debias the supercell density clusters found near the NEXRAD radar locations by taking density as a function of range to mitigate the current bias. Once the debiasing has been completed, a manual validation using a modified version of Hocker and Basara’s approach will be completed to verify whether a supercell thunderstorm percentage “hotspot” is present in Northeast Colorado from 2005 to 2007, as was found in a previous study. The results will be applied to the debiased US Great Plains supercell thunderstorm climatology.
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