Multiparameter Radar Measurements in Colorado Convective Storms. Part II: Hail Detection Studies

Bringi, V. N.; Vivekanandan, J.; Tuttle, John D.

doi:10.1175/1520-0469(1986)043<2564:mrmicc>2.0.co;2

Cited by 81 publications

(46 citation statements)

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“…Therefore, we use the equilibrium shape in (1) in this study. We also assume that the mean and the standard deviation (SD) of the canting angle are 0°, as suggested by observations (Hendry and McCormick 1976), although some observational and theoretical studies suggest that the standard deviation of the canting angles of rain drops is likely not 0°but less than 10° (Beard and Jameson 1983;Bringi and Chandrasekar 2001;Ryzhkov et al 2002). Assuming 0°SD can lead to the overestimation of K DP and Z DR by less than 6%, this could be tolerated considering the large uncertainties in DSD (Ryzhkov et al 2002).…”

Section: ϫ4mentioning

confidence: 99%

“…These properties also directly affect radar measurements within each radar sampling volume. Additional observational parameters available from polarimetric Doppler radars, including differential reflectivity and differential phase measurements can be very helpful here as they contain information about the density, shape, orientation, and DSDs of hydrometeors (Doviak and Zrnic 1993;Bringi and Chandrasekar 2001).…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

Jung

Zhang

Xue

2008

Monthly Weather Review

146

162

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A radar simulator for polarimetric radar variables, including reflectivities at horizontal and vertical polarizations, the differential reflectivity, and the specific differential phase, has been developed. This simulator serves as a test bed for developing and testing forward observation operators of polarimetric radar variables that are needed when directly assimilating these variables into storm-scale numerical weather prediction (NWP) models, using either variational or ensemble-based assimilation methods. The simulator takes as input the results of high-resolution NWP model simulations with ice microphysics and produces simulated polarimetric radar data that may also contain simulated errors. It is developed based on calculations of electromagnetic wave propagation and scattering at the S band of wavelength 10.7 cm in a hydrometeor-containing atmosphere. The T-matrix method is used for the scattering calculation of raindrops and the Rayleigh scattering approximation is applied to snow and hail particles. The polarimetric variables are expressed as functions of the hydrometeor mixing ratios as well as their corresponding drop size distribution parameters and densities. The presence of wet snow and wet hail in the melting layer is accounted for by using a new, relatively simple melting model that defines the water fraction in the melting snow or hail. The effect of varying density due to the melting snow or hail is also included. Vertical cross sections and profiles of the polarimetric variables for a simulated mature multicellular squall-line system and a supercell storm show that polarimetric signatures of the bright band in the stratiform region and those associated with deep convection are well captured by the simulator.

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Section: ϫ4mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

Jung

Zhang

Xue

2008

Monthly Weather Review

146

162

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“…2.7b,c). This is consistent with the Z DR hole observed in the microburst studied by Wakimoto and Bringi (1988) and the convective storm studied by Bringi et al (1986a).…”

Section: Simulated Radar Fields For a Supercell Stormsupporting

confidence: 89%

“…Negative Z DR is also set to 0 as we assume that the differential attenuation is small for S-band radars at both polarizations, which could cause negative Z DR by attenuating Z H more than Z V . Also, the negative Z DR observed from hail (Bringi et al 1986a;Illingworth et al 1987), either from prolate or conical shape particles or three-body scattering (Hubbert and Bringi 1997), is not simulated in this study; they are less important because they will most likely fall below our threshold. We also note here that setting the negative value to zero is also a form of data thresholding; we believe doing so is desirable and can be done with real data also.…”

Section: Simulation Of Observations and The Error Modelmentioning

confidence: 87%

State and parameter estimation

Yavin¹

Numerical Studies in Nonlinear Filtering

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“…Seliga et al, 1982;. The difference has been confirmed by rigorous computations of multi-parameter observables for backscattering from a model hail distribution (Bringi et al, 1986) and by in situ measurements performed by Husson & Pointin (1989). More recently, Z DR has been used to infer hail size in storm regions where Z DR is nearly zero (Aydin et al, 1990;Balakrishnan & Zrnic, 1990;Zrnic et al, 1993).…”

Section: Introductionmentioning

confidence: 62%

Detection of hail by means of polarimetric radar data and hailpads: results from four storms

Nanni¹,

Mezzasalma²,

Alberoni³

2000

Meteorological Applications

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Hailfall is one of the most dangerous and damaging phenomena associated with severe thunderstorms. Its remote identification is still under investigation, especially by means of polarimetric radar data. The need to use polarimetric measurements is a limit to operational implementation of the techniques developed up till now. The routine sampling of radar allows the computation of the differential hail signal HDR (Aydin et al., 1986) every 15 minutes. The assessment of the HDR is performed by comparison with the ground truth from a hailpad network. The spatial resolution of this network is quite coarse (one pad per 16 km2) for the collection of all the hail shafts, as they generally have a small horizontal extent and are short lived. Such bounds have been considered in the analysis of four large hailfalls recorded in 1997. This study reveals also the limitation on radar hail detection arising from the frequent occurrence of strong differential attenuation. Nevertheless, to identify regions where a sharp rain–hail transition occurs, polarimetric measurements work much better than algorithms based only on horizontal reflectivity. Results show that use of HDR gives a high probability of correct hail detection as well as an acceptable number of false alarms. Copyright © 2000 Royal Meteorological Society

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Multiparameter Radar Measurements in Colorado Convective Storms. Part II: Hail Detection Studies

Cited by 81 publications

References 0 publications

Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

State and parameter estimation

Detection of hail by means of polarimetric radar data and hailpads: results from four storms

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