Identifying Updrafts with ZDR Hotspots

High ZDR values found above the ambient freezing level can be created by large drops and wet hail suspended in strong convective updrafts (e.g., Kumjian et al. 2014; Snyder et al. 2015). This polarimetric signature, known as the ZDR column, has been investigated in a multitude of observational and modeling studies (e.g. Hubbert et al. 1998; Snyder et al. 2017; Montopoli et al. 2021; Wilson and Vanderbroke 2021) and shown to be largely co-located with thunderstorm updrafts (e.g., Snyder et al. 2017; Klaus et al. 2023). Identifying the updraft and measuring its characteristics makes available many potential applications including nowcasting of thunderstorm intensification and discrimination between severe and non-severe storms (e.g., Kuster et al 2019, 2020).

Despite various efforts to design algorithms for automated ZDR column detection (e.g., Snyder et al. 2015; Kingfield and Picca 2018), none of them have yet been implemented in regular operations due to limited computational resources and suboptimal (i.e., incomplete) sampling strategies in operational networks that tend to produce frequent misdetections. The Snyder et al. (2015) algorithm is vulnerable to ZDR miscalibration, and all ZDR column detection techniques can be contaminated by hail spikes or size sorting along the inflow. Furthermore, ZDR columns may underestimate updraft height and width if large hail with intrinsic low ZDR is present (e.g., Snyder et al. 2017) or if the mid levels of the storm were sampled poorly.

We present a robust and computationally inexpensive technique to identify ZDR columns and updraft locations for real-time operations. It is based on the detection of ZDR “hotspots” on a single CAPPI level (e.g., -10°C) and is independent of ZDR calibration errors. Besides the use in operational nowcasting, further potential applications include systematic analyses of the relationship between ZDR column metrics and storm strength, the initialization of NWP models, and the creation of training datasets for artificial intelligence (AI) models. Our hotspot technique may also be applied to other radar fields, such as low cross correlation aloft which is used to detect large hail or “hotspots” of KDP which indicates the possibility of KDP columns and downbursts (Kuster et al. 2021).

References

Hubbert, J., V. N. Bringi, L. D. Carey, and S. Bolen, 1998: CSU-CHILL Polarimetric Radar Measurements from a Severe Hail Storm in Eastern Colorado. J. Appl. Meteor., 37, 749–775, https://doi.org/10.1175/1520-0450(1998)037<0749:CCPRMF>2.0.CO;2.

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.

Klaus, V., H. Rieder, and R. Kaltenböck, 2023: Insights in Hailstorm Dynamics through Polarimetric High-Resolution X-band and Operational C-band Radar: a case Study for Vienna, Austria. Monthly Weather Review, https://doi.org/10.1175/MWR-D-22-0185.1.

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. Wea. Forecasting, 35, 2507–2522, https://doi.org/10.1175/WAF-D-20-0083.1.

Kuster, C. M., J. C. Snyder, T. J. Schuur, T. T. Lindley, P. L. Heinselman, J. C. Furtado, J.W. Brogden, and R. Toomey, 2019: Rapid-update radar observations of ZDR column depth and its use in the warning decision process. Wea. Forecasting, 34, 1173–1188, https://doi.org/10.1175/WAF-D-19-0024.1.

Kuster, C. M., B. R. Bowers, J. T. Carlin, T. J. Schuur, J. W. Brogden, R. Toomey, and A. Dean, 2021: Using K DP Cores as a Downburst Precursor Signature. Wea. Forecasting, 36, 1183–1198, https://doi.org/10.1175/WAF-D-21-0005.1.

Montopoli, M., E. Picciotti, L. Baldini, S. Di Fabio, F. S. Marzano, and G. Vulpiani, 2021: Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar. Atmospheric Research, 264, 105852, https://doi.org/10.1016/j.atmosres.2021.105852.

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.

Snyder, J. C., H. B. Bluestein, D. T. Dawson II, and Y. Jung, 2017: Simulations of Polarimetric, X-Band Radar Signatures in Supercells. Part II: Z DR Columns and Rings and K DP Columns. J. Appl. Meteor. Climatol., 56, 2001–2026, https://doi.org/10.1175/JAMC-D-16-0139.1.

Wilson, M. B., and M. S. Van Den Broeke, 2022: Using the Supercell Polarimetric Observation Research Kit (SPORK) to Examine a Large Sample of Pretornadic and Nontornadic Supercells. EJSSM, 17, 1–38, https://doi.org/10.55599/ejssm.v17i2.85.

2.0.CO;2.\n\nKingfield, 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.\n\nKlaus, V., H. Rieder, and R. Kaltenböck, 2023: Insights in Hailstorm Dynamics through Polarimetric High-Resolution X-band and Operational C-band Radar: a case Study for Vienna, Austria. Monthly Weather Review, https://doi.org/10.1175/MWR-D-22-0185.1.\n\nKumjian, 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.\n\nKuster, C. M., T. J. Schuur, T. T. Lindley, and J. C. Snyder, 2020: Using ZDR columns in forecaster conceptual models and warning decision making. Wea. Forecasting, 35, 2507–2522, https://doi.org/10.1175/WAF-D-20-0083.1.\n\nKuster, C. M., J. C. Snyder, T. J. Schuur, T. T. Lindley, P. L. Heinselman, J. C. Furtado, J.W. Brogden, and R. Toomey, 2019: Rapid-update radar observations of ZDR column depth and its use in the warning decision process. Wea. Forecasting, 34, 1173–1188, https://doi.org/10.1175/WAF-D-19-0024.1.\n\nKuster, C. M., B. R. Bowers, J. T. Carlin, T. J. Schuur, J. W. Brogden, R. Toomey, and A. Dean, 2021: Using K DP Cores as a Downburst Precursor Signature. Wea. Forecasting, 36, 1183–1198, https://doi.org/10.1175/WAF-D-21-0005.1. \n\nMontopoli, M., E. Picciotti, L. Baldini, S. Di Fabio, F. S. Marzano, and G. Vulpiani, 2021: Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar. Atmospheric Research, 264, 105852, https://doi.org/10.1016/j.atmosres.2021.105852.\n\nSnyder, 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.\n\nSnyder, J. C., H. B. Bluestein, D. T. Dawson II, and Y. Jung, 2017: Simulations of Polarimetric, X-Band Radar Signatures in Supercells. Part II: Z DR Columns and Rings and K DP Columns. J. Appl. Meteor. Climatol., 56, 2001–2026, https://doi.org/10.1175/JAMC-D-16-0139.1.\n\nWilson, M. B., and M. S. Van Den Broeke, 2022: Using the Supercell Polarimetric Observation Research Kit (SPORK) to Examine a Large Sample of Pretornadic and Nontornadic Supercells. EJSSM, 17, 1–38, https://doi.org/10.55599/ejssm.v17i2.85.\n"}" data-sheets-userformat="{"2":513,"3":{"1":0},"12":0}">