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Amanda M. Murphy, Alexander Ryzhkov, and Pengfei Zhang

Abstract

A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

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Cameron R. Homeyer, Thea N. Sandmæl, Corey K. Potvin, and Amanda M. Murphy

Abstract

An improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells.

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Amanda M. Murphy, Robert M. Rauber, Greg M. McFarquhar, Joseph A. Finlon, David M. Plummer, Andrew A. Rosenow, and Brian F. Jewett

Abstract

An analysis of the microphysical structure of elevated convection within the comma head region of two winter cyclones over the midwestern United States is presented using data from the Wyoming Cloud Radar (WCR) and microphysical probes on the NSF/NCAR C-130 aircraft during the Profiling of Winter Storms campaign. The aircraft penetrated 36 elevated convective cells at various temperatures T and distances below cloud top z d. The statistical properties of ice water content (IWC), liquid water content (LWC), ice particle concentration with diameter > 500 μm N >500, and median mass diameter D mm, as well as particle habits within these cells were determined as functions of z d and T for active updrafts and residual stratiform regions originating from convective towers that ascended through unsaturated air. Insufficient data were available for analysis within downdrafts.

For updrafts stratified by z d, distributions of IWC, N >500, and D mm for all z d between 1000 and 4000 m proved to be statistically indistinct. These results imply that turbulence and mixing within the updrafts effectively distributed particles throughout their depths. A decrease in IWC and N >500 in the layer closest to cloud top was likely related to cloud-top entrainment.

Within residual stratiform regions, decreases in IWC and N >500 and increases in D mm were observed with depth below cloud top. These trends are consistent with particles falling and aggregating while entrainment and subsequent sublimation was occurring.

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Cameron R. Homeyer, Alexandre O. Fierro, Benjamin A. Schenkel, Anthony C. Didlake Jr., Greg M. McFarquhar, Jiaxi Hu, Alexander V. Ryzhkov, Jeffrey B. Basara, Amanda M. Murphy, and Jonathan Zawislak

Abstract

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models.

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