On the Influence of Assumed Drop Size Distribution Form on Radar-Retrieved Thunderstorm Microphysics

2006 ◽  
Vol 45 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Edward A. Brandes ◽  
Guifu Zhang ◽  
Juanzhen Sun
Keyword(s):  
Drop Size ◽  
Liquid Water Content ◽  
Polarimetric Radar ◽  
Stratiform Rain ◽  
Distribution Form ◽  
Median Volume ◽  
Strong Convection ◽  
Radar Measurements ◽  
Size Model ◽  
Drop Size Distributions

Abstract Polarimetric radar measurements are used to retrieve drop size distributions (DSD) in subtropical thunderstorms. Retrievals are made with the single-moment exponential drop size model of Marshall and Palmer driven by radar reflectivity measurements and with a two-parameter constrained-gamma drop size model that utilizes reflectivity and differential reflectivity. Results are compared with disdrometer observations. Retrievals with the constrained-gamma DSD model gave better representation of total drop concentration, liquid water content, and drop median volume diameter and better described their natural variability. The Marshall–Palmer DSD model, with a fixed intercept parameter, tended to underestimate the total drop concentration in storm cores and to overestimate significantly the concentration in stratiform regions. Rainwater contents in strong convection were underestimated by a factor of 2–3, and drop median volume diameters in stratiform rain were underestimated by 0.5 mm. To determine possible DSD model impacts on numerical forecasts, evaporation and accretion rates were computed using Kessler-type parameterizations. Rates based on the Marshall–Palmer DSD model were lower by a factor of 2–3 in strong convection and were higher by about a factor of 2 in stratiform rain than those based on the constrained-gamma model. The study demonstrates the potential of polarimetric radar measurements for improving the understanding of precipitation processes and microphysics parameterization in numerical forecast models.

scholarly journals Raindrop Size Distribution and Rain Characteristics during the 2013 Great Colorado Flood

2015 ◽  
Vol 17 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Katja Friedrich ◽  
Evan A. Kalina ◽  
Joshua Aikins ◽  
Matthias Steiner ◽  
David Gochis ◽  
...  
Keyword(s):  
Drop Size ◽  
Doppler Radar ◽  
Liquid Water Content ◽  
Size Distributions ◽  
Intense Rainfall ◽  
Wind Fields ◽  
Raindrop Size Distribution ◽  
Median Volume ◽  
Drop Size Distributions ◽  
Analysis System

Abstract Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (<40 dBZ), differential reflectivity Zdr (<1.3 dB), specific differential phase Kdp (<1° km−1), and D0 (<1 mm). In addition, high liquid water content was present throughout the entire event. Raindrops observed in the plains were generally larger than those in the foothills. DSDs observed in the foothills were characterized by a large concentration of small-sized drops (d < 1 mm). Heavy rainfall rates with slightly larger drops were observed during the first intense rainfall episode (0000–0800 UTC 12 September) and were associated with areas of enhanced low-level convergence and vertical velocity according to the wind fields derived from the Variational Doppler Radar Analysis System. The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during the 2013 Great Colorado Flood. As a result, Z–R relations commonly used by the operational NEXRAD strongly underestimated rainfall rates by up to 43%.

scholarly journals Do We Observe Aerosol Impacts on DSDs in Strongly Forced Tropical Thunderstorms?

2011 ◽  
Vol 68 (9) ◽  
pp. 1902-1910 ◽  
Author(s):  
P. T. May ◽  
V. N. Bringi ◽  
M. Thurai
Keyword(s):  
Drop Size ◽  
Probability Distributions ◽  
Size Distributions ◽  
Cloud Properties ◽  
Median Volume ◽  
Rain Drop ◽  
Distribution Parameters ◽  
Radar Measurements ◽  
Ice Multiplication ◽  
Drop Size Distributions

Abstract Rain drop size distributions retrieved from polarimetric radar measurements over regularly occurring thunderstorms over the islands north of Darwin, Australia, are used to test if aerosol contributions to the probability distributions of the drop size distribution parameters (median volume diameter and normalized intercept parameter) are detectable. The observations reported herein are such that differences in cloud properties arising from thermodynamic differences are minimized but even so may be a factor. However, there is a clear signature that high aerosol concentrations are correlated with smaller number concentrations and larger drops. This may be associated with enhanced ice multiplication processes for low aerosol concentration storms or other processes such as invigoration of the updrafts.

scholarly journals Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 360 ◽  
Author(s):  
Elisa Adirosi ◽  
Nicoletta Roberto ◽  
Mario Montopoli ◽  
Eugenio Gorgucci ◽  
Luca Baldini
Keyword(s):  
Drop Size ◽  
Weather Radar ◽  
Size Distributions ◽  
Regression Methods ◽  
Radar Meteorology ◽  
Quality Checks ◽  
Radar Measurements ◽  
Drop Size Distributions ◽  
First And Second Generation ◽  
Theoretical Considerations

Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed.

Drop deformation estimate with multi-polarization radar

2020 ◽  
Vol 12 (9) ◽  
pp. 870-877
Author(s):  
Yuliya Averyanova ◽  
Anna Rudiakova ◽  
Felix Yanovsky
Keyword(s):  
Drop Size ◽  
Mathematical Expression ◽  
Microwave Remote Sensing ◽  
Radar Signal ◽  
Drop Deformation ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Energy Redistribution ◽  
Drop Size Distributions ◽  
Radar Antenna

AbstractThis paper considers the ability of polarization measurements for microwave remote sensing of clouds and precipitation. The simulation of reflections from liquid hydrometeors with a multi-polarization radar system is presented. The mathematical expression of energy received by a radar antenna with arbitrary polarization is obtained. The simulation of the energy redistribution of the signal reflected from liquid hydrometeors assembled over the antennas of multi-polarimetric radar for different wind conditions and different drop-size distributions is obtained and analyzed. The simulation results demonstrate the possibility to register wind and wind-related phenomena by polarimetric radar. The results of the paper can also be used to exclude an impact of drop vibration or oscillation into the radar signal to eliminate errors and underestimation during parameter measurements. The approach to segregate the reflected signal magnitude variations due to the wind-related phenomena from other factors is discussed.

scholarly journals Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

2020 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Jui Le Loh ◽  
Dong-In Lee ◽  
Mi-Young Kang ◽  
Cheol-Hwan You
Keyword(s):  
Vertical Profile ◽  
Drop Size ◽  
Classification Scheme ◽  
Radar Data ◽  
Number Concentration ◽  
Polarimetric Radar ◽  
Retrieval Method ◽  
Median Volume ◽  
Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

Shipborne Polarimetric Weather Radar: Impact of Ship Movement on Polarimetric Variables at C Band

2014 ◽  
Vol 31 (7) ◽  
pp. 1557-1563 ◽  
Author(s):  
M. Thurai ◽  
P. T. May ◽  
A. Protat
Keyword(s):  
Drop Size ◽  
Size Distributions ◽  
Correction Factors ◽  
Distribution Parameters ◽  
Radar Measurements ◽  
Nonzero Mean ◽  
Polarimetric Weather Radar ◽  
Drop Size Distributions ◽  
Differential Reflectivity ◽  
Ship Movement

Abstract The effect of ship motion on shipborne polarimetric radar measurements is considered at C band. Calculations are carried out by (i) varying the “effective” mean canting angle and (ii) separately examining the elevation dependence. Scattering from a single oblate hydrometeor is considered at first. Equations are derived (i) to convert the measured differential reflectivity for nonzero mean canting angles to those for zero mean canting angle and (ii) to do the corresponding corrections for nonzero elevation angles. Scattering calculations are also performed using the T-matrix method with measured drop size distributions as input. Dependence on mean volume diameter is examined as well as variations of the four main polarimetric parameters. The results show that as long as the ship movement is limited to a roll of less than about 10°–15°, the effects are tolerable. Furthermore, the results from the scattering simulations have been used to provide equations for correction factors that can be applied to compensate for the “apparent” nonzero canting angles and nonzero elevation angles, so that drop size distribution parameters and rainfall rates can be estimated without any bias.

scholarly journals Retrieval of Arbitrarily Shaped Raindrop Size Distributions from Wind Profiler Measurements

2005 ◽  
Vol 22 (4) ◽  
pp. 433-442 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Ahoro Adachi
Keyword(s):  
Drop Size ◽  
Large Data ◽  
Doppler Spectrum ◽  
Wind Profiler ◽  
Size Distributions ◽  
Vertical Profiles ◽  
Large Variability ◽  
Median Volume ◽  
Raindrop Size ◽  
Drop Size Distributions

Abstract An efficient iterative retrieval method for arbitrarily shaped raindrop size distributions (ITRAN) is developed for Doppler spectra measured with a wind profiler. A measured Doppler spectrum is a convolution of the precipitation spectrum and the turbulent spectrum. Deconvolution of the Doppler spectra is achieved through repeated convolutions. The developed method assumes no prior shape of drop size distributions and automatically obtains raindrop size distributions; additionally, it can be applied to large data volumes. Furthermore, it is insensitive to initial values. The method was applied to both simulated and observed spectra. Derived drop size distributions agree with simulated values. Narrower turbulent spectral widths yield better results. Integral values of median volume diameter (D0), liquid water content (LWC), and radar reflectivity factor are estimated with errors of less than 10%. Accurate vertical profiles of raindrop size distributions result when this method is applied to wind profiler data. The technique performed very well with most observed spectra. Some recovered spectra departed from the corresponding measured spectra, for cases in which a clear-air peak could not be accurately reproduced because of uncertainties in the location of the minimum position between the clear-air echo and the precipitation echo. Statistical relationships between LWC and integral rainfall parameters yield interesting features. The median volume diameter is statistically independent of the LWC and is associated with the large variability of the total number of drops, NT, between events. Vertical profiles from one event show a clear inverse relationship between NT and D0

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