Multiparameter radar and in situ aircraft observation of graupel and ...

570 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 38, NO. 1, JANUARY 2000 

Multiparameter Radar and in situ Aircraft Observation 

of Graupel and Hail 

Abou El-Magd, V. Chandrasekar, V. N. Bringi, and Walter Strapp 

Abstract—Documenting simultaneous multiparameter radar 

observations of precipitation in conjunction with in situ hydrometeor 

sampling is important for the interpretation of 

multiparameter radar observations. In situ observation using 

aircraft-mounted probes is one of the best ways to collect such 

data. In situ observation of hail and graupel in convective storms 

is complicated due to adverse environment of flight and low 

concentration of large particles that are difficult to sample. This 

paper presents one of the first observations of simultaneous 

multiparameter radar observations and in situ samples of wet hail 

and graupel in convective storms. The observations are unique 

because of the excellent coordination between aircraft samples 

and radar scanning, as well as relatively large sample volumes 

of aircraft data. Multiparameter radar observations (namely 

reflectivity, differential reflectivity, linear depolarization ratio, 

copolar correlation coefficient, and specific differential phase) are 

documented in graupel and wet hail. The observations indicate 

that the linear depolarization ratio and copolar correlation 

measurements, in conjunction with reflectivity levels, can be used 

to distinguish between graupel and hail. A simple procedure is 

developed to estimate the average bulk density of graupel and wet 

hail, comparing radar and in situ observations. 

Index Terms—Graupel, hail, in situ measurements. 

I. INTRODUCTION 

REMOTE identification and classification of precipitation 

particle types is a problem of important practical significance. 

Several studies of radar observations suggest that the 

polarimetric measurements can be used effectively to discriminate 

between precipitation phases. Bringi et al. [1], used reflectivity 

at horizontal polarization and differential reflectivity 

measurements for distinguishing between the liquid 

and ice regions of clouds. Their technique is based on the differences 

in backscattering cross sections of rain and ice particles 

at horizontal and vertical polarization. Hall et al. [2] used 

and to identify the hydrometer types. Aydin et al. 

[3] propose a hail-detection signal using simulated radar 

data from distrometer measurements. is the departure of 

the observed from the hail–rain boundary in the – 

space. Tong et al. [4] provided a scheme to quantitatively separate 

rain and ice in mixed phase precipitation. Verification and 

validation of multiparameter radar signatures of hydrometeors 

Manuscript received October 19, 1998; revised April 30, 1999.This work 

was supported by the National Science Foundation (ATM-9413453 and ATM- 

9730321). Support for HVPS instrumentation was provided by the DOD-Center 

for Geosciences at CSU. 

A. El-Magd, V. Chandrasekar, and V. N. Bringi are with the Colorado State 

University, Fort Collins, CO 80523 USA (e-mail: chandra@engr.colostate.edu). 

W. Strapp is with the Atmospheric Environment Services, Downsview, Ont., 

Canada. 

Publisher Item Identifier S 0196-2892(00)00412-5. 

is important for interpretation of the radar data. In situ measurements 

using aircraft-mounted particle sampling instrumentation 

is the best way to validate and quantify multiparameter radar remote 

sensing techniques. Extensive experimental results were 

documented by Chandrasekar et al. [5] and Bringi et al. [6] to 

characterize polarization diversity measurements in rain using 

simultaneous observations of multiparameter radar and in situ 

aircraft measurements. Such observations are virtually absent in 

hail storms. This paper reports one of the first results of simultaneous 

observations of multiparameter radar and in situ aircraft 

in hail storms and graupel. One of the most important factors 

that has a significant impact on interpreting multiparamter radar 

signatures and in situ comparisons of ice particles is the density, 

which can vary over a wide range depending on the particle type. 

The smallest densities are seen in snow-type particles, whereas 

hail particles typically have very high density. In this paper, we 

present in situ comparison of multiparameter radar signatures in 

graupel and hail regions with particular attention to evaluation 

of density of graupel and hail, while documenting multiparameter 

radar signatures under different hydrometeor conditions. 

Our paper is organized as follows. Section II presents a summary 

of multiparameter radar measurements. In Section III, the 

instrumentation used in this research and data sources are described. 

Section IV presents in situ comparison between radar 

and aircraft data in hail, and Section V presents a similar comparison 

in graupel. The important observations of the paper are 

summarized in Section VI. 

II. MULTIPARAMETER RADAR MEASUREMENTS OF 

PRECIPITATION 

The size distribution, shape, orientation distribution, and thermodynamic 

phase state of precipitation particles form the fundamental 

building block describing precipitation medium. The 

various measurements from a multiparameter radar can be described 

in terms of the properties of the precipitation medium 

characteristics as follows. 

The reflectivity factors at Horizontal and Vertical polarization 

states can be described by and 

where is the particle size distribution, is the 

radar cross section at and polarization, is the wavelength, 

and , , and are the complex dielectric 

constants of the hydrometeors. Thus, the reflectivity 

of the precipitation depends on the particle shape, orientation, 

(1) 

0196–2892/00$10.00 © 2000 IEEE

EL-MAGD et al.: MULTIPARAMETER RADAR AND IN SITU AIRCRAFT OBSERVATION 571 

and thermodynamic phase as well as the transmitted wave polarization 

state. The differential reflectivity is defined as 

the ratio of the reflectivity at horizontal and vertical polarizations 

and is given by 

TABLE I 

CSU-CHILL RADAR CHARACTERISTICS 

Seliga and Bringi [7] show that there are differences in the 

backscattering cross sections of rain at horizontal and vertical 

polarizations. They exploited the existence of a relationship 

between raindrop shape and size and showed that in rain 

is related to the median volume diameter in rainfall. The 

specific differential phase is defined as [8] 

(2) 

where and are the forward scattering amplitudes for horizontally 

and vertically polarized waves, and indicates a real 

part of a complex number. The one-way differential propagation 

phase between two range locations and is defined 

as 

(3) 

(4) 

Thus, is the specific differential phase propagation between 

the horizontal and vertical polarization. In the case of 

rain, is a measure of the mean of the mass-weighted axis 

ratio of the drops and is approximately related to the fourth moment 

of raindrop size distribution. 

The correlation coefficient between horizontally and 

vertically polarized returns is defined as 

where and are the backscatter amplitudes at and 

states. The is mainly influenced by the variability in the 

ratio of the vertical-to-horizontal size of individual hydrometeors. 

When particle size is large compared to the wavelength, 

the is affected significantly by the differential phase shift 

upon scattering between the horizontal and vertical states. The 

value of is for rain. In a mixture of different hydrometeors, 

the is reduced due to the broader spread in the 

composite distribution of shapes and sizes compared to a distribution 

of single hydrometeor type. The decrease in correlation 

would be largest if the reflectivity-weighted distribution of the 

two hydrometeor types is comparable. 

The ratio of the cross-polar signal power to the copolar power, 

linear depolarization ratio (LDR) is defined as 

(5) 

(6) 

where is the vertically polarized backscatter amplitude 

when the incident wave is at horizontal state. The LDR depends 

on the orientation, shape, and size distributions as well as on the 

composition of scatterers. 

III. DATA SOURCES AND INSTRUMENTATION 

The data presented in this paper were collected by the 

CSU-CHILL radar and the T-28 aircraft equipped with the high 

volume particle sampler (HVPS) probe. In the following, we 

describe the details of the instrumentation and the data set. 

A. CSU-CHILL Radar 

CSU-CHILL radar is an S-band cm dual linear-polarization, 

pulsed-Doppler radar. The radar is equipped with an 

antenna of 1 beam with matched patterns at horizontal and 

vertical polarizations. The radar has a high power signal processor 

that can calculate the multiparamemter radar measurements 

such as , , , and , LDR and Doppler 

velocity in real time. The characteristic features of the radar 

relevant to this paper are listed in Table I. 

B. Aircraft Observations 

The aircraft data used in this paper were collected by the T-28 

aircraft, operated by South Dakota School of Mines and Technology 

(SDSM&T), Rapid City. The T-28 is an armored stormpenetrating 

research aircraft. It carries a suite of state-of-the-art


Fig. 1. 

HVPS probe installed under the right wing of the T-28 aircraft. 

Fig. 3. Storm cell location, June 22, 1995. 

Fig. 2. 

HVPS optical design. 

instrumentation that can accurately characterize microphysical 

populations ranging from cloud droplets a few microns in diameter 

to hailstones the size of golf balls. During the summer of 

1995, the T-28 aircraft was equipped with an HVPS instrument. 

The HVPS is capable of measuring particles sizes up to 5 cm, by 

taking two-dimensional (2-D) digital pictures of hydrometeors 

that pass through a 4.5 cm 20 cm plane that is normal to the 

direction of aircraft flight. This plane is a curtain of light that is 

projected onto a 256 pixel, linear array, which is sampled at a 

rate proportional to the speed of the aircraft. The pixel spacing in 

the sample plane is 200 m. As particles pass through the light 

plane, they create shadows on the linear array that are converted 

by a one-bit analog to digital converter. Thus, as a particle passes 

through the sample plane, the sequential slices produce a digital 

2-Dimage of the particle. Fig. 1 shows the high-volume spectrometer 

installed under the right wing of the T-28, and Fig. 2 

shows the the optical design of the HVPS. 

Fig. 4. T28 flight tracks, June 22, 1995. 

C. Data Description 

1) June 22, 1995 Hailstorm: A severe hailstorm occurred 

on June 22, 1995 near Fort Collins, CO. This storm grew to 

a height of 12.5 km, and upon collapsing, produced heavy 

rain and hail with maximum sizes of 3–4 cm. Fig. 3 shows 

a sample plan position indicator (PPI) of the reflectivity 

factor. We can see from Fig. 3 that the intense part of the 

storm is located at 45–50 km range to the northeast of the 

CSU-CHILL radar. The radar continuously scanned the storm 

with approximately a 2 min resolution for about an hour. At the 

same time, the T-28 aircraft made several penetrations through 

the storm, collecting samples of hail stones. On 22 June, the 

T-28 made four flights through the storm cell located 35 km


(a) 

(b) 

(c) 

(d) 

(e) 

Fig. 5. Vertical section of radar measurements for the June 22, 1995 hail storm: (a) Z , (b) Z , (c) , (d) K , and (e) LDR. 

northeast of the radar (Fig. 3). The flights were at altitudes 

between 2.5–3.5 km above ground level to collect data in the 

hail region. Fig. 4 shows the T-28 flight tracks during the 

time interval 17:26:19 to 17:45:19 universal time (UT). The 

storm was characterized by heavy rain mixed with hail. Near 

zero dB, was observed on-ground coincident with high 

, indicating that the hail shaft extended to the ground. 

Fig. 5 shows a vertical section through the storm. The various 

panels (a)–(e) show reflectivity, , , , and LDR, 

respectively. The vertical section of reflectivity shows strong 

vertical development with reflectivities in excess of 60 dB up 

to 7.5 km altitude. The measurement shows rainfall in 

the front end of the storm (35–38 km), followed by hail. This 

inference is also confirmed by and LDR signatures. The 

region from 42 km and beyond is marked by hail mixed with 

rain on the ground, indicated by high and low .In 

summary, the vertical section of radar data shown in Fig. 5 

indicates it is a severe hail storm.


Fig. 7. T-28 aircraft flight tracks, June 20, 1995. 

Fig. 6. Storm cell location, June 20, 1995. 

2) June 20, 1995 Storm: On June 20, 1995, the T-28 aircraft 

made extensive penetrations through a convective cell, which 

formed 20 km east of the radar. Fig. 6 shows a sample PPI of reflectivity 

factor. Most of the penetrations were made at constant 

altitudes of 4 km above ground level. The T-28 flight tracks are 

shown in Fig. 7. The CSU-CHILL measurements show that this 

storm had echoes in excess of 40 dBZ along the T-28 tracks. The 

HVPS images of this storm showed conical graupel particles of 

sizes up to 1.4 cm. 

Thus, the data sources provide in situ observations of precipitation 

particles such as rain, hail, and graupel. The data set is 

unique in several aspects, namely 

a) large sample volume data using aircraft; 

b) hail samples using aircraft in high reflectivity; 

c) simultaneous coordinated multiparameter radar measurements. 

In the following section, we present detailed analysis of comparison 

between radar and in situ aircraft observations. 

IV. IN SITU COMPARISON OF RADAR AND AIRCRAFT 

MEASUREMENTS IN RAIN–HAIL MIXTURE 

The constant altitude PPI (CAPPI) of the storm is shown in 

Fig. 8 at the altitude of the aircraft corresponding to the flight 

timings of the T-28 penetrations. Also shown on the CAPPI are 

the T-28 flight tracks. During the flight, the HVPS collected 

samples of hydrometeors along the path. Sample images of hydrometeors 

from HVPS are shown in Fig. 9. We can see the 

presence of smooth hail particles. Based on the HVPS observations 

along the flight track, the size distributions were estimated. 

Fig. 10 shows sample size distributions along the path. For a 

given particle size distribution, the multiparameter radar observations 

can be estimated using (1)–(6). One of the unknowns in 

this process that cannot be provided by HVPS image is the density 

of the ice particles. For a given size distribution, the reflectivity 

is very sensitive to density of ice particles in comparison to 

other parameters such as shape or orientation distribution. This 

can be demonstrated by the following example. Consider a precipitation 

medium where rain is mixed with hail, and the rain 

and hail distribution are as follows. 

1) Rain Distribution: Marshall Palmer exponential distribution 

with rainfall rate of 75 mm/hr−1. The shape of raindrops is 

described by equilibrium shape [9]. 

2) Hail Distribution: Exponential hail distribution with a 

hailfall rate of 30 mm hr−1 [10]. The shape of hail is modeled as 

spheroidal, with an orientation distribution. Fig. 11 shows the 

variation of as a function of axis ratio of hail particles and 

bulk density for the model assumed here. We can see from the results 

of Fig. 11 that reflectivity factor is most sensitive to density 

of ice particles in comparison to other factors such as shape and 

orientation distribution. Therefore, to match reflectivity measurements, 

it is sufficient (as a first approximation) to vary density 

and keep the other parameters such as shape and orientation 

constant as those of spherical particles. We have used the following 

procedure to compare radar and aircraft measurements. 

Based on experimentally observed distributions, we compute reflectivity 

according to (1). We assume spherical shape for hail 

and compute the reflectivity factor. The delineation between 

rain and hail particles was made with combinations of information 

such as HVPS images of large particles, discontinuities 

in particle size distributions, and large deviations from equilibrium 

shape of raindrops. The reflectivities of rain and hail 

portions are calculated and added to compute the total reflectivity. 

The density of hail is varied so that the in situ simulated 

reflectivity is matched pointwise to the reflectivity observed 

by the radar. Fig. 12(a) shows the comparison of reflectivities 

assuming a spherical model for hail in the rain/hail mixture. 

Fig. 12(b) shows the corresponding best estimates of density of 

hail for each sampled distribution along the path. We can see that


Fig. 8. 

Simultaneous aircraft track and radar data (CAPPI) for June 22, 1995 storm. 

the density estimates are in the range expected for hail. Once the 

density is fixed, the best shape and orientation parameters are 

chosen to match the LDR and measurements, assuming an 

average hail particle density. The average density is obtained as 

the mean of the estimates shown in Fig. 12(b) to be 0.932 g/cm3. 

The corresponding axis ratio of hail particles was assumed to be 

about 0.7 with a Gaussian canting angle distribution of 30 standard 

deviation. The value of LDR measured by the radar and 

inferred by the aircraft measurement is about −22 dB. The 

values observed were nearly zero, and observations were 

about 0.93. In principle, the problem of matching multiparameter 

radar and in situ observations from aircraft-based particle 

size distributions in the hail region is complicated. However, 

we have developed a simple first-order approximation to match 

them varying the average density of hail particles. 

V. IN SITU COMPARISON OF RADAR AND AIRCRAFT 

MEASUREMENTS IN THE GRAUPEL REGION OF CONVECTIVE 

STORM 

On June 20, 1995, the T-28 collected extensive in situ data in 

the ice phase of a storm consisting predominantly of graupel 

particles. Most of the aircraft flights through the storm were 

made at a constant altitude of 4 km above ground. Fig. 13 shows 

CAPPI of the storm at the altitude of the aircraft, at the timings 

of the T-28 flights. The flight tracks are also shown on Fig. 13.


(a) 

Fig. 11. Variation of simulated Z signature with axis ratio and bulk density 

for rain/hail mixture. 

(b) 

Fig. 9. (a) HVPS images of hydrometeors and (b) 2D-HVPS hail image, 

17:28:31, June 22, 1995 at 2.5 km altitude. 

(a) 

Fig. 10. Particle size distribution 17:30:19 to 17:31:19 on June 20, 1995 

measured by HVPS. 

During the flight the HVPS collected samples of hydrometeors 

along the path. Sample images of hydrometeors from HVPS are 

shown in Fig. 14. We can see from Fig. 14 that the particles were 

conical graupel. From the HVPS images, the size distributions 

were estimated along the T-28 flight track. Fig. 15 shows sample 

size distribution along the path. Similar to the procedure used in 

the last section, we try to match the reflectivities measured by 

radar with that simulated from in situ measured-particle size distribution. 

The density of the ice particles is varied for each observation 

point to match the reflectivities. The radar-measured 

(b) 

Fig. 12. (a) Radar and in situ comparison of Z , June 22, 1995 and (b) 

estimated hail density along the T-28 flight track, June 22, 1995. 

reflectivity and the inferred reflectivity from aircraft measurements 

are shown in Fig. 16(a), whereas the corresponding density 

values to match the reflectivities are shown in Fig. 16(b). We 

can see from Fig. 16 that on the average, the density of graupel 

is inferred to be about 0.55 g/cm3. The graupel particles were


(a) 

(b) 

Fig. 14. (a) The 2D-HVPS graupel image, 16:17:25, June 20, 1995 at 4.0 km 

altitude and (b) 2D-HVPS graupel image, 16:32:30, June 20, 1995 at 4.0 km 

altitude. 

Fig. 13. Simultaneous aircraft track and radar data (CAPPI) for June 20, 1995 

storm. 

modeled as conical shapes with smooth edges and random orientation 

[9]. The average LDR values in graupel measured by 

the radar and inferred by the aircraft measurement is about −26 

dB, whereas the values were close to zero dB, and the 

observations were close to 0.97. 

VI. SUMMARY AND CONCLUSION 

Documenting the in situ observation of hydrometeor type in 

conjunction with multiparameter radar measurements is of great 

Fig. 15. Sample particle size distribution based on HVPS images from the 

June 20, 1995 storm, 165 710 to 165 740. 

importance in the interpretation of multiparameter radar measurements. 

This paper reports one of the first measurements 

of , , LDR, and in conjunction with in situ hydrometeor 

observations in hail and graupel in convective storms. 

The T-28 is a unique armored aircraft that is capable of flying 

through hail storms. The in situ observations were unique with 

respect to the HVPS. Prior in situ observations with the T-28 

were conducted with 2D-P probes that did not have the adequate 

sample volume to sample large hail particles. The large 

sample volume of the HVPS probe was critical in sampling the 

low concentration of large hail particles. The in situ observation 

and the corresponding multiparameter radar observations of the


being zero completes the information. Thus, it appears 

that a combination of LDR, , and reflectivity measurements 

can potentially be used in a classification scheme to identify wet 

hail and graupel. The problem of matching radar measurements 

and in situ observation based on simulation of radar measurements 

is complicated due to a wide variability of shape and 

the orientation distribution of particles and their density. Nevertheless, 

we have used a simple approach to make a quantitive 

comparison of radar and in situ observations. The quantative 

comparison yielded a density estimate of wet hail of about 

0.93 g/cm3 and of graupel of about 0.55 g/cm3. Thus, the in 

situ verification provides information on the bulk density of ice 

particles in addition to documenting the particle type and size 

distribution. 

(b) 

ACKNOWLEDGMENT 

The authors acknowledge the assistance provided by the 

SDSM&T staff in decoding the HVPS data and H. Liu in the 

preparation of this manuscript. 

Fig. 16. (a) Radar and in situ comparison of Z and (b) June 20, 1995, 

estimated graupel density; June 20, 1995. 

(b) 

TABLE II 

COMPARISON OF THE MICROPHYSICAL PROPERTIES OF JUNE 20,1995 AND 

JUNE 22, 1995 STORMS 

REFERENCES 

[1] V. N. Bringi, R. M. Rasmussen, and J. Vivekanandan, “Multiparameter 

radar measurements in Colorado convective storms Part I,” Graupel 

Melting Studies, vol. 43, pp. 2545–2563, 1986. 

[2] M. P. M. Hall, J. W. F. Goddard, and S. M. Cherry, “Identification of 

hydrometeors and other targets by dual-polarization radar,” Radio Sci., 

vol. 19, no. 1, pp. 132–140, 1984. 

[3] K. Aydin, T. A. Seliga, and V. Balaji, “Remote sensing of hail with a 

dual linear polarization radar,” J. Climate Appl. Meteorol., vol. 25, pp. 

1475–1484, 1986. 

[4] “Multiparameter radar observations of time evolution of convective 

storms”, to be published. 

[5] V. Chandrasekar, W. A. Cooper, and V. N. Bringi, “Axis ratios and oscillations 

of raindrops,” J. Atmos. Sci., vol. 45, pp. 1323–1333, 1988. 

[6] V. N. Bringi, V. Chandrasekar, and R. Xiao, “Raindrop axis ratios and 

size distributions: An assesment of multiparameter radar algorithms,” 

IEEE Trans. Geosci. Remote Sensing, vol. 36, no. 3, pp. 703–715, 1998. 

[7] T. A. Seliga and V. N. Bringi, “Potential use of radar differential reflectivity 

measurements at orthogonal polarizations for measuring precipitation,” 

J. Appl. Meteorol., vol. 15, pp. 69–76, 1976. 

[8] , “Differential reflectivity and differential phase shift: Applications 

in radar meteorology,” Radio Sci., vol. 13, pp. 271–275, 1978. 

[9] H. R. Pruppacher and J. D. Klett, Microphysics of Clouds and Precipitation. 

Dordrecht, The Netherlands: D. Reidel. 

[10] L. Cheng and M. English, “A relationship between hailstones concentration 

and size,” J. Atmos. Sci., vol. 40, pp. 204–213, 1983. 

[11] K. Aydin, T. A. Seliga, and V. N. Bringi, “Differential radar scattering 

properties of model hail and mixed-phase hydrometeors,” in Radio Sci., 

1984, vol. 19, ch. Ch. 29a, pp. 58–66. 

Abou El-Magd, photograph and biography not available at the time of publication. 

V. Chandrasekar, photograph and biography not available at the time of publication. 

two cases are summarized in Table II. Based on the results of 

Table II, we can see that critically distinguishing features of 

these two cases are found in observations of LDR and 

in conjunction with reflectivity levels. The values of and 

V. N. Bringi, photograph and biography not available at the time of publication. 

Walter Strapp, photograph and biography not available at the time of publication.

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