Applications and Examples of a Behavior-Based Tornadic Debris Signature Identification Technique (36)
Kevin Skow, NOAA/NWS Topeka, KS, Topeka, Kansas
Ever since the tornadic debris signature (TDS) term was coined in the early 2000s, the meteorological community has used set thresholds of horizontal reflectivity, cross-polar correlation coefficient, and differential reflectivity, coupled with a subjectively-determined radial velocity couplet, to ascertain the presence of debris being lofted into a radar sample volume. Early definitions were based on small sample sizes from polarimetric research radars, and it was not until 2013 that the National Weather Service (NWS) completed the polarimetric upgrade of its Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Since this upgrade, the research and operational criteria for classifying a TDS have gradually relaxed, but still rely on thresholds of various base products (with the possibility of TDSs not meeting these thresholds mentioned as an aside).
From 2013-2015, the NWS increased the WSR-88D half-degree base product update times from every five minutes to as frequently as every ninety seconds in select Volume Coverage Patterns. These new temporal upgrades now allow meteorologists to diagnose the behavioral characteristics of a lofted debris plume during the lifespan of a tornado and also reveal the need to refine the definition of a TDS once again. Using this improved radar dataset and ground/aerial tornado track verification from case studies of multiple storm modes, this study will show the need for a TDS classification system that relies on identifying relative depressions in cross-polar correlation coefficient and/or increases in horizontal reflectivity compared to the surrounding data field, which persist for multiple elevation angles and/or scan times. This study will also show how meteorologists can identify these signals along favored regions for tornadogenesis, including boundaries for landspouts and updraft-downdraft convergence zones for quasi-linear convective system tornadoes, where a traditional radial velocity couplet may not be observed in WSR-88D data. This plume identification technique using cross-polar correlation coefficient and horizontal reflectivity behavioral trends can help reduce false alarms from other signatures that may appear similar to TDSs, such as hail cores, inflow, and updraft columns.