80 Severe Weather Radar Signature Characteristics and the Vertical Distribution of Shear and Buoyancy

Tuesday, 23 October 2018
Stowe & Atrium rooms (Stoweflake Mountain Resort )
Michael A. Magsig, NOAA/NWS, Norman, OK

Volumetric radar base data analysis is a core part of National Weather Service (NWS) severe weather warning decision making taught by the Warning Decision Training Division as part of a holistic process including environmental analysis/numerical model guidance, spotter reports, and other sensors such as total lightning and satellite data. The relatively rapid evolution of severe weather requires NWS warning forecasters to quickly identify relatively simple commonly observed severe weather precursors to generate lead time for the onset of severe weather. Numerous radar signature precursors exist in Dual-Polarization Weather Service Radar 1988 Doppler (WSR-88D) data, including high reflectivities at high heights and cold temperatures, storm-top divergence, rotation, low-level and mid-level convergence, differential reflectivity columns and arcs, correlation coefficient minima, strong ground-relative low-altitude winds, high values of specific differential phase associated with intense rainfall, and more. At near ranges and far ranges from the radar, significant sampling limitations exist in WSR-88D data due to radar beam widths, beam heights, and gaps between radar tilts which can have profound effects on detecting these critical cues and resultant warning decision making. Little guidance exists on characteristic heights and depths of these radar signatures and their relationship to the vertical distributions of buoyancy and shear driving the production of severe weather. This study aims to document the characteristic height and depths of these common warning decision making critical cues and relate them in new ways to the underlying vertical distribution of shear and buoyancy. Relating these signatures to their fore environment affords an opportunity for forecasters to anticipate detection of critical warning cues and build stronger severe weather conceptual models, ultimately leading to improving warning decision making skill.
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