Hailstone Size Distributions and Their Relationship to Storm Thermodynamics

Cheng, Lisa Lai‐Shen; English, Marianne; Wong, Raymond Chi-Wing

doi:10.1175/1520-0450(1985)024<1059:hsdatr>2.0.co;2

Cited by 46 publications

(24 citation statements)

References 11 publications

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“…In many bulk model simulations, size distributions of hail are approximated by an exponential distribution with interdependent slope, which is usually justified by the analysis of the observed hailstone size distributions at the surface. For example, Cheng et al (1985) proposed the following empirical formula for hail-size distributions near the ground: same time the size distribution calculated for the L_CCN case is approximated better using L 5 0.6 mm 21 . Analysis of Figs.…”

Section: F the Shape Of Hail Number Concentration Distribution Functmentioning

confidence: 81%

“…Owing to low values of hail concentration, collection of the required data demands that the size distributions of hail particles should be measured along long flight tracks of many tens of kilometers to get the necessary statistics. To get statistically significant hailsize distributions during surface measurements at particular points, the measurements should be performed over a comparatively long period and averaged over many samples (Cheng and English 1983;Cheng et al 1985). At the same time, hail is obviously distributed nonuniformly over clouds and the sizes of falling hail change with time.…”

Section: F the Shape Of Hail Number Concentration Distribution Functmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Aerosols on Freezing Drops, Hail, and Precipitation in a Midlatitude Storm

Ilotoviz

Хаин

Benmoshe

et al. 2015

Journal of the Atmospheric Sciences

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A midlatitude hail storm was simulated using a new version of the spectral bin microphysics Hebrew University Cloud Model (HUCM) with a detailed description of time-dependent melting and freezing. In addition to size distributions of drops, plate-, columnar-, and branch-type ice crystals, snow, graupel, and hail, new distributions for freezing drops as well as for liquid water mass within precipitating ice particles were implemented to describe time-dependent freezing and wet growth of hail, graupel, and freezing drops.Simulations carried out using different aerosol loadings show that an increase in aerosol loading leads to a decrease in the total mass of hail but also to a substantial increase in the maximum size of hailstones. Cumulative rain strongly increases with an increase in aerosol concentration from 100 to about 1000 cm 23 . At higher cloud condensation nuclei (CCN) concentrations, the sensitivity of hailstones' size and surface precipitation to aerosols decreases. The physical mechanism of these effects was analyzed. It was shown that the change in aerosol concentration leads to a change in the major mechanisms of hail formation and growth. The main effect of the increase in the aerosol concentration is the increase in the supercooled cloud water content. Accordingly, at high aerosol concentration, the hail grows largely by accretion of cloud droplets in the course of recycling in the cloud updraft zone. The main mechanism of hail formation in the case of low aerosol concentration is freezing of raindrops.

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Section: F the Shape Of Hail Number Concentration Distribution Functmentioning

confidence: 81%

Section: F the Shape Of Hail Number Concentration Distribution Functmentioning

confidence: 99%

Effect of Aerosols on Freezing Drops, Hail, and Precipitation in a Midlatitude Storm

Ilotoviz

Хаин

Benmoshe

et al. 2015

Journal of the Atmospheric Sciences

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show abstract

“…The PSD parameters and canting angle distributions for hail differ from other particles (Cheng and English 1983;Bringi and Chandrasekar 2001). In rain, for example, the size limits for the integration of the PSD are typically 0.1 to max(3D 0 , 8) mm.…”

Section: B Hail Modelmentioning

confidence: 87%

Simulating Radar Observations of Precipitation at Higher Frequencies from Lower-Frequency Polarimetric Measurements

Fritz

Chandrasekar

2012

Journal of Atmospheric and Oceanic Technology

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The translation of radar data from one frequency to another based on electromagnetic absorption and scattering models has been used to convert S-band measurements to higher frequencies, such as those within the X or Ku bands, in order to create realistic simulations. As the target frequency of simulations for weather radar increase to X band and above, the size of large raindrops and hail either approach or exceed the radar wavelength, resulting in a radar cross section that is no longer in the linear Rayleigh region. The Mie solution to scattering models is then required. With the advent of dual-polarization systems, a more complete characterization of the effect of precipitation on radar is possible in order to improve model effectiveness. However, typical curve-or surface-fitting methods still have limitations as particle size increases. A more robust solution is presented here in the form of a neural network that incorporates the nonlinear relationship among various polarimetric observation variables and the radar wavelength and look angle. Thus, high-frequency observations of a convective storm containing hail can be simulated using polarimetric ground radar measurements. Adequate polarimetric data from hail storms at high frequencies do not exist, however, so network outputs for this case can only be compared to theoretical observations. An application of the simulation procedure to characterize the effect of precipitation on spaceborne synthetic aperture radar (SAR) using ground observations is presented.

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“…Model error is often blamed as the dominant source in the loss of forecast skill, especially inaccuracies in the model microphysical parameterizations. Observational studies indicate that the densities and intercept parameters of hydrometeor distributions can vary widely among storms and even within a single storm (Joss and Waldvogel 1969;Pruppacher and Klett 1978;Knight et al 1982;Ziegler et al 1983;Cheng et al 1985;Cifelli et al 2000). Therefore, assuming a fixed set of microphysical parameters in a convection-permitting numerical model can lead to significant errors in not only the forecasts but also the analyses of severe storm events.…”

Section: Statusmentioning

confidence: 99%