On Polarimetric Radar Signatures of Deep Convection for Model Evaluation: Columns of Specific Differential Phase Observed during MC3E*

Lier-Walqui, Marcus van; Fridlind, A. M.; Ackerman, A. S.; Collis, Scott; Helmus, Jonathan; MacGorman, Donald R.; North, Kirk; Kollias, Pavlos; Posselt, Derek J.

doi:10.1175/mwr-d-15-0100.1

Cited by 45 publications

(48 citation statements)

References 95 publications

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“…The squall‐line MCS case simulated in this study occurred on 20 May 2011, during the Midlatitude Continental Convective Clouds Experiment (MC3E) [ Petersen and Jensen , ; Jensen et al ., ], which was supported by both the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM) and NASA's Global Precipitation Measurement mission ground validation program in north‐central Oklahoma from 22 April to 6 June 2011. Detailed ground‐based and aircraft cloud dynamical and microphysical observations are available for this case [ Mather and Voyles , ; Jensen et al ., ], which has been a focus of several recent studies [e.g., Tao et al ., , ; Giangrande et al ., ; Fan et al ., ; Kumjian et al ., ; Marinescu et al ., ; Van Lier‐Walqui et al ., ; Saleeby et al ., ; Fridlind et al ., ]. Section 2 includes a more detailed description of the case and the relevant observations used in this study.…”

Section: Introductionmentioning

confidence: 99%

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

et al. 2017

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An intercomparison study of a midlatitude mesoscale squall line is performed using the Weather Research and Forecasting (WRF) model at 1 km horizontal grid spacing with eight different cloud microphysics schemes to investigate processes that contribute to the large variability in simulated cloud and precipitation properties. All simulations tend to produce a wider area of high radar reflectivity (Ze > 45 dBZ) than observed but a much narrower stratiform area. The magnitude of the virtual potential temperature drop associated with the gust front passage is similar in simulations and observations, while the pressure rise and peak wind speed are smaller than observed, possibly suggesting that simulated cold pools are shallower than observed. Most of the microphysics schemes overestimate vertical velocity and Ze in convective updrafts as compared with observational retrievals. Simulated precipitation rates and updraft velocities have significant variability across the eight schemes, even in this strongly dynamically driven system. Differences in simulated updraft velocity correlate well with differences in simulated buoyancy and low‐level vertical perturbation pressure gradient, which appears related to cold pool intensity that is controlled by the evaporation rate. Simulations with stronger updrafts have a more optimal convective state, with stronger cold pools, ambient low‐level vertical wind shear, and rear‐inflow jets. Updraft velocity variability between schemes is mainly controlled by differences in simulated ice‐related processes, which impact the overall latent heating rate, whereas surface rainfall variability increases in no‐ice simulations mainly because of scheme differences in collision‐coalescence parameterizations.

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Section: Introductionmentioning

confidence: 99%

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

et al. 2017

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“…The K DP columns (i.e., positive K DP values that are above the 0°C level) have been well studied and documented Loney et al 2002;Ryzhkov et al 2005;Kumjian et al 2010;van Lier-Walqui et al 2016). Many times, there is a spatial offset between the positive Z DR and K DP columns (Kumjian and Ryzhkov 2008), but here, they are coincident with the K DP column extending to nearly the top of the Z DR column.…”

Section: Polarimetric Variable Interpretationmentioning

confidence: 99%

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

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Weckwerth

et al. 2018

Bulletin of the American Meteorological Society

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The National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

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“…Following the convection, stratiform, and anvil classification, radar echo is evaluated to determine whether or not signatures indicative of strong convective updrafts are present. To identify convective updrafts, the SL3D algorithm searches for three well-known radar signatures: 1) weak-echo regions [WERs; bounded or unbounded, e.g., Browning and Donaldson (1963); Musil et al (1986); Calhoun et al (2013)], 2) Z DR columns (e.g., Caylor and Illingworth 1987;Illingworth 1988;Bringi et al 1991;Conway and Zrnić 1993;Ryzhkov et al 1994;Brandes et al 1995;Loney et al 2002;Scharfenberg et al 2005;Kumjian and Ryzhkov 2008;Kumjian et al 2014), and 3) K DP columns (e.g., Zrnić et al 2001;Loney et al 2002;Kumjian and Ryzhkov 2008;Van Lier-Walqui et al 2016). WERs are elements of a convective storm with relatively low Z H values at lower altitudes that are at least partially bounded horizontally and above by relatively high Z H values.…”

Section: Convective Updraftmentioning

confidence: 99%

“…The K DP columns, on the other hand, indicate updrafts that have lofted large concentrations of moderately sized (2-4 mm) raindrops above the freezing level (e.g., Loney et al 2002). Van Lier-Walqui et al (2016) found that K DP columns are not only a good indicator of the presence of an updraft, but changes in the volume of a K DP column are correlated to changes in the updraft mass flux. Snyder et al (2015) have recently developed an algorithm for objectively identifying Z DR columns in single-radar observations.…”

Section: Convective Updraftmentioning

confidence: 99%

Storm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm

2017

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This study presents a new storm classification method for objectively stratifying three-dimensional radar echo into five categories: convection, convective updraft, precipitating stratiform, nonprecipitating stratiform, and ice-only anvil. The Storm Labeling in Three Dimensions (SL3D) algorithm utilizes volumetric radar data to classify radar echo based on storm height, depth, and intensity in order to provide a new method for updraft classification and improve upon the limitations of traditional storm classification algorithms. Convective updrafts are identified by searching for three known polarimetric radar signatures: weak-echo regions (bounded and unbounded) in the radar reflectivity factor at horizontal polarization ([Formula: see text]), differential radar reflectivity ([Formula: see text]) columns, and specific differential phase ([Formula: see text]) columns. Additionally, leveraging the three-dimensional information allows SL3D to improve upon missed identifications of weak convection and intense stratiform rain in traditional two-dimensional classification schemes. This study presents the results of applying the SL3D algorithm to several cases of high-resolution three-dimensional composites of NEXRAD WSR-88D data in the contiguous United States. Comparisons with a traditional algorithm that uses two-dimensional maps of [Formula: see text] are also shown to illustrate the differences of the SL3D algorithm.

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On Polarimetric Radar Signatures of Deep Convection for Model Evaluation: Columns of Specific Differential Phase Observed during MC3E*

Cited by 45 publications

References 95 publications

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

Storm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm

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