Linking ZDR Hotspots to Storm Cell Tracking

Convective updrafts are often accompanied by high ZDR several kilometers above the ambient freezing level, forming so-called “ZDR columns” (e.g., Kumjian et al. 2014). There is evidence that some properties of ZDR columns such as maximum altitude, areal extent at certain levels or the magnitude of ZDR itself are linked to updraft strength, location, and structure, revealing important information about a storm’s imminent future, such as its potential to develop large hail or the severity of any tornadoes produced by the storm (e.g., Kuster et al. 2020; French and Kingfield 2021). The automatic detection and tracking of ZDR columns has been the goal of multiple studies in recent years, but each proposed method has limitations, typically false detections due to ZDR miscalibration, contamination with hail spikes, confusion with the melting layer, or poor vertical resolution of the sampled volume (e.g., Snyder et al. 2015; Kingfield and Picca 2018).

We present results of the combined application of the novel calibration-independent ZDR hotspot method with the cell tracking system MCIT (Hu et al. 2019; Krause and Klaus 2023, in review). This synergy increases the reliability of the updraft detection by removing spurious ZDR column detections located away from cell cores. The combination of MCIT and ZDR column data also allows a systematic analysis of the relation between the updraft (as detected by the ZDR column) and other storm properties routinely calculated by MCIT. Our approach facilitates the analysis of updrafts throughout a storm’s lifetime; assigning updrafts to larger and longer lived MCIT based cells results in a significant improvement over tracking the comparatively small and volatile ZDR hotspot signature (updraft) itself.

We examine the relative locations of updraft and cell core under different environmental conditions, and based on the tracking of MCIT and ZDR columns over time we try to identify precursors for storm intensification and the development of large hail. We show the potential of the method to identify the fallout of large hail when intense ZDR hotspots vanish over short time spans.

Literature
French, M. M., and D. M. Kingfield, 2021: Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area. Weather and Forecasting, https://doi.org/10.1175/WAF-D-21-0087.1.

Hu, J. et al., 2019: Tracking and characterization of convective cells through their maturation into stratiform storm elements using polarimetric radar and lightning detection. Atmospheric Research, 226, 192–207, https://doi.org/10.1016/j.atmosres.2019.04.015.

Kingfield, D. M., and J. C. Picca, 2018: Development of an Operational Convective Nowcasting Algorithm Using Raindrop Size Sorting Information from Polarimetric Radar Data. Weather and Forecasting, 33, 1477–1495, https://doi.org/10.1175/WAF-D-18-0025.1.

Kumjian, M. R., A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips, 2014: The Anatomy and Physics of ZDR Columns: Investigating a Polarimetric Radar Signature with a Spectral Bin Microphysical Model. Journal of Applied Meteorology and Climatology, 53, 1820–1843, https://doi.org/10.1175/JAMC-D-13-0354.1.

Kuster, C. M., T. J. Schuur, T. T. Lindley, and J. C. Snyder, 2020: Using ZDR Columns in Forecaster Conceptual Models and Warning Decision-Making. Weather and Forecasting, 35, 2507–2522, https://doi.org/10.1175/WAF-D-20-0083.1.

Snyder, J. C., A. V. Ryzhkov, M. R. Kumjian, A. P. Khain, and J. Picca, 2015: A ZDR column detection algorithm to examine convective storm updrafts. Wea. Forecasting, 30, 1819–1845, https://doi.org/10.1175/WAF-D-15-0068.1.