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1. Hydrometeor Size Sorting in the Asymmetric Eyewall of Hurricane Matthew (2016)

   https://doi.org/10.1029/2020JD032671  

   Laurencin, Chelsey N. ; Didlake, Jr., Anthony C. ; Loeffler, Scott D. ; Kumjian, Matthew R. ; Heymsfield, Gerald M. ( September 2020 , Journal of Geophysical Research: Atmospheres)

   Polarimetric radar observations of Hurricane Matthew's asymmetric eyewall were captured by WSR‐88D radars from 1500 UTC on 7 October 2016 to 0000 UTC on 8 October 2016. Raindrop size sorting was observed within the eyewall, marked by a differential reflectivity (Z_DR) enhancement region situated upwind of a specific differential phase (K_DP) enhancement region, both overlapping the maximum reflectivity. This signature indicated that the largest raindrops fell out of the eyewall updrafts faster than the smaller, abundant drops that were advected further downstream by the primary circulation. Airborne Doppler radar observations revealed an updraft structure in an azimuthal location consistent with the size‐sorting signature and previous observational studies of eyewall kinematic asymmetries.

   Given that a tropical cyclone's environment or internal dynamics can modulate the eyewall's kinematic and microphysical structure, we used a simple size‐sorting model that only includes sedimentation and advection of raindrops by the axisymmetric tangential wind to examine how an eyewall size‐sorting signature responds to artificial changes in the tangential wind speed and initial raindrop size distributions (DSDs). The axisymmetric tangential wind was retrieved from WSR‐88D radar observations using the Ground‐Based Velocity Track Display technique. The simple model was capable of producing an eyewall size‐sorting signature with an azimuthal separation between the simulated Z_DRand K_DPenhancements in general agreement with the observed separation (~20°) at low levels. Sensitivity tests showed that the azimuthal separation between the Z_DRand K_DPenhancements responded to changes in the tangential wind speed, but not to changes in the initial DSDs aloft.

    

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2. Storm-Scale Polarimetric Radar Signatures Associated with Tornado Dissipation in Supercells

   https://doi.org/10.1175/WAF-D-21-0067.1  

   ( January 2022 , Weather and Forecasting)

   Polarimetric radar data from the WSR-88D network are used to examine the evolution of various polarimetric precursor signatures to tornado dissipation within a sample of 36 supercell storms. These signatures include an increase in bulk hook echo median raindrop size, a decrease in midlevel differential radar reflectivity factor (Z_DR) column area, a decrease in the magnitude of theZ_DRarc, an increase in the area of low-level large hail, and a decrease in the orientation angle of the vector separating low-levelZ_DRand specific differential phase (K_DP) maxima. Only supercells that produced “long-duration” tornadoes (with at least four consecutive volumes of WSR-88D data) are investigated, so that signatures can be sufficiently tracked in time, and novel algorithms are used to isolate each storm-scale process. During the time leading up to tornado dissipation, we find that hook echo median drop size (D₀) and medianZ_DRremain relatively constant, but hook echo medianK_DPand estimated number concentration (N_T) increase. TheZ_DRarc maximum magnitude andZ_DR–K_DPseparation orientation angles are observed to decrease in most dissipation cases. Neither the area of large hail nor theZ_DRcolumn area exhibit strong signals leading up to tornado dissipation. Finally, combinations of storm-scale behaviors and TVS behaviors occur most frequently just prior to tornado dissipation, but also are common 15–20 min prior to dissipation. The results from this study provide evidence that nowcasting tornado dissipation using dual-polarization radar may be possible when combined with TVS monitoring, subject to important caveats.

    

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3. A Polarimetric Radar Analysis of Ice Microphysical Processes in Melting Layers of Winter Storms Using S-Band Quasi-Vertical Profiles

   https://doi.org/10.1175/JAMC-D-19-0128.1  

   Griffin, Erica M. ; Schuur, Terry J. ; Ryzhkov, Alexander V. ( April 2020 , Journal of Applied Meteorology and Climatology)

   Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z_H. QVP ML statistics are examined for two regimes in the ML data: Z_H≥ 20 dB Z and Z_H< 20 dB Z. Regions of Z_H≥ 20 dB Z indicate locations of MLs collocated with enhanced differential reflectivity Z_DRand reduced copolar correlation coefficient ρ_hv, while for Z_H< 20 dB Z a well-defined ML is difficult to discern using Z_Halone. Evidence of large Z_DRup to 4 dB, backscatter differential phase δ up to 8°, and low ρ_hvdown to 0.80 associated with lower Z_H(from −10 to 20 dB Z) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K_DPand maximum Z_Hin the ML; these are the first QVP observations of K_DPin MLs documented at S band. Negative correlation occurs between minimum ρ_hvin the ML and ML depth and between minimum ρ_hvin the ML and the corresponding enhancement of Z_H(Δ Z_H= Z_Hmax− Z_Hrain).

    

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4. Polarimetric Relations for Snow Estimation—Radar Verification

   https://doi.org/10.1175/JAMC-D-19-0140.1  

   Bukovčić, Petar ; Ryzhkov, Alexander ; Zrnić, Dusan ( May 2020 , Journal of Applied Meteorology and Climatology)

   null (Ed.)

   Abstract In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), and , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S ( K DP , Z dr ) polarimetric relation utilizing the ratio between specific differential phase K DP and differential reflectivity Z dr , , is derived. Both K DP and are proportionally affected by the ice particles’ aspect ratio and width of the canting angle distribution; therefore, the variables’ ratio tends to be almost invariant to the changes in these parameters. The S ( K DP , Z ) and S ( K DP , Z dr ) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S ( Z ) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S ( Z ), indicating good potential for more reliable radar snow measurements. 

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5. Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms

   https://doi.org/10.1175/MWR-D-23-0196.1  

   Chen, Haiqin ; Gao, Jidong ; Sun, Tao ; Chen, Yaodeng ; Wang, Yunheng ; Carlin, Jacob T. ( April 2024 , Monthly Weather Review)

   The differential reflectivity (Z_DR) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor (q_υ) observations are retrieved from observedZ_DRcolumns under the assumption that humidity is saturated within the convection whereZ_DRcolumns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo-q_υobservations fromZ_DRcolumns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that theZ_DRcolumns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo-q_υobservations leads to an enhancement ofq_υwithin the convection, while concurrently reducing humidity in no-rain areas. Sensitivity experiments indicate that a tuned smaller observation error and a shorter horizontal decorrelation scale are optimal for a better assimilation of pseudo-q_υfromZ_DRcolumns, resulting in more stable rain rates during short-term forecasts. Additionally, a 15-min cycling assimilation frequency yields the best performance, providing the most accurate reflectivity forecast in terms of both location and intensity. Analysis of thermodynamic fields reveal that assimilatingZ_DRcolumns provides more favorable initial conditions for sustaining convection, including sustainable moisture condition, a strong cold pool, and divergent winds near the surface, consequently enhancing reflectivity and precipitation. With the optimal configuration determined from the sensitivity tests, a quantitative evaluation further demonstrates that assimilating the pseudo-q_υobservations fromZ_DRcolumns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.

    

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