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1. Polarimetric Observations and Simulations of Sublimating Snow: Implications for Nowcasting

   https://doi.org/10.1175/JAMC-D-21-0038.1  

   Carlin, Jacob T.; Reeves, Heather D.; Ryzhkov, Alexander V. (July 2021, Journal of Applied Meteorology and Climatology)

   Abstract Snow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity Z DR slightly decreases, and copolar-correlation coefficient ρ hv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase K dp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snowretrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to six hours in advance, with a 6-h median bias of just -18.5 minutes. A more detailed case study of the 08 December 2013 snowstorm in the Philadelphia region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process. 

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2. 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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3. Ice Hydrometeor Shape Estimations Using Polarimetric Operational and Research Radar Measurements

   https://doi.org/10.3390/atmos11010097  

   Matrosov, Sergey Y. (January 2020, Atmosphere)

   A polarimetric radar method to estimate mean shapes of ice hydrometeors was applied to several snowfall and ice cloud events observed by operational and research weather radars. The hydrometeor shape information is described in terms of their aspect ratios, r, which represent the ratio of particle minor and major dimensions. The method is based on the relations between depolarization ratio (DR) estimates and aspect ratios. DR values, which are a proxy for circular depolarization ratio, were reconstructed from radar variables of reflectivity factor, Ze, differential reflectivity, ZDR, and copolar correlation coefficient ρhv, which are available from radar systems operating in either simultaneous or alternate transmutation of horizontally and vertically polarized signals. DR-r relations were developed for retrieving aspect ratios and their sensitivity to different assumptions and model uncertainties were discussed. To account for changing particle bulk density, which is a major contributor to the retrieval uncertainty, an approach is suggested to tune the DR-r relations using reflectivity-based estimates of characteristic hydrometeor size. The analyzed events include moderate snowfall observed by an operational S-band weather radar and a precipitating ice cloud observed by a scanning Ka-band cloud radar at an Arctic location. Uncertainties of the retrievals are discussed. 

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

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

   Segall, Jacob H.; French, Michael M.; Kingfield, Darrel M.; Loeffler, Scott D.; Kumjian, Matthew R. (January 2022, Weather and Forecasting)

   Abstract 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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