3 multi-sensor measurements of raindrop size distribution...

Study of the variability of the rainfall microstructure. A comparison using multi-sensor measurements.

3 Multi-Sensor Measurements ofRaindrop Size Distribution at NASA

Wallops Flight Facility

3.1 Introduction

Knowledge of DSD is essential in determining the characteristics of precipitation.

Precipitation is an integral product of DSD and is highly variable in space and time. The

variability of precipitation is directly linked to the variability of DSD.

Disdrometer that measures the DSD at a point on the ground is a solo source in determining

the variability of DSD between different climatological regions, between different storms and

within different regimes of a storm.

In the Tropics more small drops and less large drops were found in oceanic precipitation than

in continental precipitation at the given reflectivity. Similarly, more small drops and less large

drops were found in remnants of a tropical cyclone than in frontal precipitation in mid-latitude

site at the same reflectivity. The presence of small drops was higher in deep convective regime

than in stratiform regime in a tropical convection at the same rain rate (Tokay and Short 1996).

The pronounced differences and similarities of the DSD listed above has a wide range of

applications in atmospheric sciences, hydrology, and agricultural and soil sciences.

In the use of weather radar, a relationship between radar measured reflectivity, Z and surface

rain rate, R has been traditionally derived employing DSD measurements.

Chapter 3: Multi-Sensor Measurements of Raindrop Size Distribution at NASAWallops Flight Facility 23

Multi-sensor measurements of Raindrop Size Distribution

Despite the fact the literature has abundant Z-R relations, the US National Weather Service

operationally uses a single relation Z=300·R1.6

except for tropical sites where Z=250·R1.4

is

employed. The characteristic differences in DSD and, therefore, in precipitation results in

significant errors in radar rainfall estimation.

Outside the radar meteorology, the characteristics of DSD play significant role in cloud

modeling and climate studies. Hydrologists, on the other hand, are interested on high temporal

scale of precipitation measurements and radar estimated rainfall is often employed as an input

in hydrological modeling. Soil erosion that is also an interest for hydrologist and soil scientists

is related to the kinetic energy of raindrops.

The variability of DSD in different climate regimes is well recognized by the NASA’s

Tropical Rainfall Measuring Mission (TRMM). The TRMM that successfully constructed

three-dimensional mapping of global precipitation within its inclination of ± 35º is now

addressing regional differences in precipitation estimates between its precipitation radar and

microwave sensors.

Following workshop right after series of TRMM field campaigns in May 2000, there was

consensus of the need of long-term disdrometric measurements to obtain the characteristics of

the DSD at a given climatic region. Unfortunately, such a measurement was only available in

Kwajalein, Republic of Marshall Islands, and oceanic ground validation site. Similar efforts

are underway in Melbourne, Florida, a coastal ground validation site. The DSD measurements

were also taken at Wallops Island, Virginia, instrument test site for the last 5 years (Tokay et

al. 2005).

While long-term disdrometric measurements are essential for adequate sample size in

extracting physical characteristics of DSD rather than statistical art-effect, the accuracy of the

measured DSD is equally important.

The accuracy of the measurements requires knowledge of shortcomings of instrument and the

presence multiple sensors at a given site. Considering the relatively high cost of disdrometer

operation, a single disdrometer operation should be back up by two collocated rain gauges.

This is considered as a minimum requirement for a disdrometer operation.

The instrument test facility at Wallops Island has an adequate setup in studying the

shortcomings of the disdrometers. The Global Precipitation Measurement (GPM) mission that

is expected to be launched in 2010 and orbit at ±70º latitudes has an interest in error

characteristics of the measurements. The GPM mission also seeks one or more reliable

disdrometers that can adequately measure small (D 1 mm), medium size (1 < D 3 mm),

and large (D > 3 mm) raindrops and mixed and frozen hydrometeors in ground validation

sites.

In this study, we evaluated three different types of disdrometers through a field campaign that

was conducted at NASA Wallops Flight Facility, Wallops Island, Virginia from May to

August 2004. A brief description of each type of disdrometer and instrument part can be found

in section 2. Section 3 summarizes the rain events and overall agreement between different

types of disdrometers and collocated rain gauges. Differences in composite spectra are taken

in consideration in section 4. Selected events are analysed minute by minute to separate

different problems; conclusive remarks are given in the last section.



3.2 A brief review of tipping bucket rain gauge, and Joss-Waldvogel, Parsivel and POSS disdrometers

A large number of drop sizing instruments have been used in the past in the measurements of

the DSD. They can be divided into different groups depending on the physical principle used:

impact disdrometers, optical disdrometers (based on imaging techniques), and Doppler radar

disdrometers. In this study the following were used:

• 2 Joss-Waldvogel disdrometers (impact type).

• 2 Parsivel disdrometers (optical type).

• 2 Precipitation Occurrence Sensor System disdrometers (Doppler radar type).

All these instruments in study are able to operate continuously and unattended. Additional

instruments or station that were used during our field campaign:

• 2 Met1 tipping bucket rain gauges

• 1 Weather station 176 m away from the site.

Following a brief summary of the characteristics and operating principles of the instruments.

3.2.1 Joss-Waldvogel disdrometer (JW)

The Joss-Waldvogel disdrometer (Figure 3.1) is an impact-type instrument manufactured by

Distromet LTD, originally developed by Joss and Waldvogel (1967) with the aim of

measuring radar reflectivity.

Figure 3.1.-Joss-Waldvogel disdrometer

Considered as a reference instrument for measurements of the DSD it has been widely used in

many field campaigns. Consists of a sensor head and signal-processing electronics. JWD

infers the size of the individual drops from the measured impact velocity of the drops through

an empirical nonlinear relationship between and fall velocity and drop diameter (Figure 3.2).

Distributed into 20 intervals and with a sampling cross-sectional area of 50 cm2 drop sizes are



detected from 0.3 to 5-5.5mm with about 5% accuracy. A calibration for each unit is necessary

to determine the exact channel boundaries. The DSD and their main bulk variables, intensity

and reflectivity, are calculate as follows:

N(Dp ) =Cp

Areap v(Dp ) Dp t(m

-3·mm

-1) (3.1)

R(mmh 1) = 6 104CpDp

3

Areap tp

= 6 104 N(Dp ) Dp3 v(Dp ) Dp

p

(3.2)

Z(dBZ) =10log10CpDp

6

v(Dp ) Areap tp

=10log10 N(Dp )Dp6 Dp

p

(3.3)

where Dp is the mid size of the pth channel (mm), Cp is the number of drops in the pth size,

Areap is the drop cross section area (m2), v(Dp) in m/s is the fall speed at a diameter Dp, Dp is

the width of the pth channel (mm) , and t is the sampling time (s).

Figure 3.2.- The size of the impacting drop is retrieved from measured impactvelocity through an empirical relationship between fall velocity-D (straight line). If

drops fall at different velocity (for example dashed lines fall velocity-Drelationship), this instrument overestimates or underestimates the size of the

drops.

It has three well-known shortcomings:

a. Underestimate the number of small drops in heavy rain, due to the ringing of the cone

when it is hit by the drops, known as the dead time effect (see for example Tokay and

Short 1996), and it also suppresses small drops due to the presence of noise.

b. Cannot distinguish size of very large drops because of the fall speed merely increase at

drop diameters above 5 mm Figure 3.2), and those are grouped in the largest size bin;



this can cause an underestimation of heavy rainy minutes where the size range of the

spectrum extends over very large drops.

c. It infers the size of the individual drops from the measured impact velocity of the drops

through an empirical relationship between fall velocity and drop diameter; velocities of

falling drops can diverge from the fixed empirical fall speed, causing an

underestimation or overestimation of drop size. If fall velocities are less than the

proposed fall speed, JWD underestimate drop diameters, and drop diameters are

overestimated if fall velocities are higher than the proposed fall speed (Figure 3.2).

3.2.2 Parsivel disdrometer

Parsivel is an optical disdrometer, manufactured by PMTech AG (Figure 3.3). It consists of an

optical sensor that produces a horizontal sheet of light 180 mm long, 27 mm wide and 1mm

thick. The sampling cross section is size dependent such that the sampling area is a product of

the length and width –D/2 of the bin width, where D is the drop diameter. Transmitter and

receiver are integrated into one tunnel housing with anti-splash protection. If no particles are

present in the light beam, the receiver outputs a constant voltage. Raindrops passing through

the measurement area causes extinction and, therefore, a reduction in the received voltage. The

amplitude and the duration of the signal deviation are measures of particles size and speed

respectively. A calibration is needed to obtain an empirical relation between size and voltage.

The measured particles are classified across two fields: D and v, each one with 32 bins, then a

number of 1024 classes are available.

Figure 3.3.-Parsivel disdrometer

Equations (3.1), (3.2) and (3.3)are modified including the measured velocity for each drop

instead of the proposed fall speed. The digital signal processor also provides a classification of

precipitation type according to the standards of the WMD such as hail, rain, drizzle, graupel,

snow and mixed form.



Figure 3.4.-Two operating Parsivel measured mean velocities during the fieldcampaign that was conducted at NASA Wallops flight Facility from May to

August 2004 in comparison with the empirical fall speed. Vertical bars correspondto percentils 16 and 84 for Parsivel1 measured velocities.

The most known problems of this instrument are the record of spurious drops,

• Drops with diameter bigger than 8 mm because of the simultaneous drops. That is,

when different drops cross the measurement area at the same time, parsivel

disdrometers record only one drop with the diameter of a drop that produce the same

extinction as the simultaneous drops.

• Drops that result from the splash on the tunnel housing (which may introduce spurious

small drops passing through the measurement area); these drops can be detected

because their measured velocity differs greatly from proposed fall velocity.

To eliminate these drops in this study it is used a matrix that rejects drops bigger than 8 mm or

drops falling at velocities that differ more than 50% of the empirical fall speed. Another

problem of these instruments is the underestimation of measured velocities especially at mid-

size drops (Figure 3.4).

3.2.3 Precipitation Occurrence Sensor System (POSS)

Precipitation Occurrence Sensor System (POSS) is a low power, continuous wave, X-band,

bistatic radar (Figure 3.5). The transmitter and receiver are housed separately and mounted on

a frame 45 cm apart and the antennas are oriented 20º from the vertical, and axes intersect

midway between them 31 cm over the horizontal plane. When a drop transit the POSS

measurement volume, a voltage signal is generated with a frequency proportional to its

Doppler velocity and an amplitude function of the drop size and its location in the space.

Therefore when the drop is falling the signal generated is varying frequency and amplitude.

POSS measures Doppler power density spectrum S(f), which is a weighted moment of the



DSD, and with a discrete approximation N(D) is calculated supposing the a certain fall

velocity.

Figure 3.5.-POSS disdrometer

Because of relatively large size of the measurement volume of POSS (Figure 3.6) random

fluctuations on the estimation of the number of drops are a second order effect of its sampling

errors.

Diameter (mm)0 1 2 3 4 5 6

Sam

ple

vo

lum

e (c

m3 /s

)

109

108

107

106

105

104

103

Parsivel2

POSS

JW1

Figure 3.6.-Sample volume for the different disdrometers. Radar disdrometershad much larger sample volume than the optical and impact distrometers

Previous works (Sheppard 1990, Sheppard and Joe 1994) noted some shortcomings as the

overestimation of small drops at winds over 6 m/s. In order to reduce this effect, a limit of



10.000 concentration drops is applied in all POSS post-processing software. Other problems

showed by POSS disdrometers are the missing minutes on heavy rain due to the lightning, and

the absorption that decrease the rate estimation.

3.2.4 Tipping bucket gauges

A tipping bucket gauge (Figure 3.7) is a reliable instrument that measures the total

precipitation for a time scale of an hour or longer. It consists on an orifice of 20 cm diameter

and a tipping bucket mechanism, where each bucket is calibrate tot tip every 0.254 mm of

rainfall.

Show both systematic and random errors. Although they are calibrated and tested by the

manufacturer, they require periodic field calibration. Calibration error is just one of the

systematic errors of the gauges, such as underestimation of rainfall due to wind, wetting,

evaporation, and splashing.

It is typically situated on the top of a wooden box or pole to prevent flooding, but ideally

should be buried in the ground where it would not be affected by winds and turbulence.

Tipping bucket rain gauges are also subject to sampling errors, which are reduced for longer

time interval of rain-rate integration.

Figure 3.7.-Tipping bucket rain gauge

3.2.5 Weather Station

Horizontal wind speed was measured in a weather station placed 176 m away from the

collocated disdrometers at a tower 10 m high, which records wind speeds minute-by-minute.

As said, horizontal wind speed has an important influence on behaviour of different

instruments. Moreover, heavy winds affects in summer period mid-Atlantic coast of United

States, and this data was useful to understand some of the performance shown by

disdrometers.



3.3 Rainfall measurements

The rainfall measurements used in this work were collected between May and August 2004 at

the NASA Wallops Flight Facility, Wallops Island, Virginia (Figure 3.8).

Figure 3.8.-NASA Wallops Flight Facility (Wallops Island, Virginia).

Figure 3.9 shows accumulations of the six disdrometers and one of the tipping bucket rain

gauges. Although only 33% of the time the rain rate was over 5 mmh-1

, 81% of the total rain

fell these rates. Moreover, 55% of the total rain fell at rain rates over 20mmh-1

, which

represents 5% of the rainy minutes. This shows the dominance of convective rain in rain

accumulation.



140 160 180 200 220 240

Julian Day

0

200

400

600

Rai

n to

tal (

mm

)Accumulated rain

G1

JW1

JW2

Parsivel1

Parsivel2

POSS1

POSS2

557.54mm

547.38mm

486.59mm

477.13mm

525.57mm

448.09mm

417.66mm

Figure 3.9.-Evolution of the accumulated rainfall for six disdrometers and onetipping bucket rain gauge during our field campaign.

One-minute disdrometric records below 0.1 mmh-1

or less than 10 drops have been considered

within the noise level and were not considered. In this study we considered separation of

events, periods of at least one hour of non-raining minutes. Event with less than three tips

were disregarded.

Based on this definition, events were calculated for all the instruments from May to August

2004. Taking JW1 as the reference, we considered same rain events those (for each

instrument) between start and ending time for JW1 events (with a margin of 30 minutes in

both boundaries). Thus, we selected events with at least 1 mm accumulation in all the

instruments.

Table 3.1 presents the cumulative rainfall for all the instruments for the selected events. Start

and ending times correspond with those measured by JW1. Note that there were two events

where the second gauge did not record anything; we kept them because the excellent

agreement showed by the gauges allowed us to work only with one gauge.



Rainfall Accumulation (mm)Event Julian

day Start EndMinutesof rain

MaximalIntensity(mm/h) G1 G2 JW1 JW2 Parsivel1 Parsivel2 POSS1 POSS2

1 140 18:00 22:05 158 50,8 9,1 9,1 10,1 9,2 7,2 7,2 5,7 5,52 146 23:34 01:10 57 91,3 20,8 20,3 18,1 16,0 18,6 17,6 32,8 31,63 147 04:08 05:07 49 68,4 16,3 16,3 13,6 12,9 15,6 15,9 41,8 41,14 156 15:52 20:52 279 5,1 3,3 3,3 3,6 2,9 3,5 3,6 3,9 3,15 156 22:04 02:18 149 19,7 8,1 8,1 4,0 2,9 8,1 9,0 9,4 8,06 174 20:50 21:59 70 41,5 9,9 9,9 10,6 9,9 7,5 7,7 6,9 6,47 178 01:02 07:42 358 19,4 10,9 11,2 12,0 10,9 8,5 8,8 8,4 6,78 189 17:19 20:10 172 6,2 4,8 4,8 5,5 5,7 4,2 4,5 4,4 4,09 194 20:05 23:50 163 57,2 24,1 23,4 25,6 23,8 19,0 20,3 7,2 14,910 196 19:45 21:12 80 33,6 7,9 7,9 8,0 8,1 6,4 6,9 10,3 10,111 200 03:48 08:49 254 151,3 67,1 65,8 62,5 57,5 55,6 68,5 47,3 47,812 200 11:50 14:24 101 23,4 4,8 4,8 5,5 4,6 4,0 4,2 4,2 3,313 205 00:49 02:14 66 45,4 8,6 8,6 9,4 9,4 7,0 8,2 6,2 6,114 205 06:21 06:50 30 84 12,2 12,2 11,6 11,7 11,0 12,0 9,5 11,015 206 05:50 08:29 71 18,8 2,0 1,8 1,7 1,4 1,9 2,1 2,0 1,616 206 10:54 22:14 651 11,4 28,2 26,7 30,0 25,3 20,9 23,4 23,6 18,117 207 09:18 10:56 42 11,5 2,0 2,0 2,3 1,9 1,8 1,8 2,4 1,618 209 22:25 01:36 148 55,4 14,0 13,5 14,3 13,5 10,1 11,3 9,6 8,719 210 04:36 05:37 62 26,5 8,6 8,4 8,6 8,1 5,8 6,5 5,9 5,120 210 12:26 18:21 257 124,4 105,2 103,1 104,1 95,3 82,6 96,9 63,2 62,921 211 02:26 05:06 77 55,3 7,4 7,4 7,4 6,7 5,1 5,5 3,9 3,522 218 12:06 20:37 280 79 18,8 0,0 20,1 17,3 16,2 18,1 17,0 14,323 219 01:03 04:56 191 6,6 3,1 0,0 3,5 3,0 2,3 2,5 2,3 1,924 226 05:45 09:35 115 47 7,4 7,4 7,6 7,2 5,1 5,0 5,2 5,025 226 10:37 15:27 172 21,9 5,3 4,6 6,7 5,8 4,4 4,1 4,3 3,826 227 04:30 05:33 45 43,2 6,1 6,1 6,3 5,5 4,7 4,3 4,1 3,527 227 11:10 00:03 697 84,4 76,7 74,9 73,0 59,1 62,0 67,7 63,1 51,028 228 17:30 00:04 305 2,4 2,5 2,5 2,8 2,3 2,0 1,8 2,0 1,729 229 01:08 05:19 193 8,1 4,8 4,8 5,3 4,5 4,0 3,6 4,4 3,630 229 06:26 09:05 146 92,3 57,4 56,1 54,0 44,4 46,2 47,4 37,1 31,6

Table 3.1.- Main characteristics of the used data set.

To start the comparison, Figure 3.9 shows the total accumulation of different instruments for

all the period. Bias between different total accumulations is used as the first measure of the

behavior between them (Table 3.2)

bias =X1 X2

X1(3.4)

where X1 and X2 are the total accumulation of all the events for an instrument (we took X1 as

the biggest one). While JW1 and Parsivel2 present an excellent agreement with the gauge,

both POSS recorded much less rain. The other JWD and Parsivel have an excellent agreement

between them and a good performance with the gauge. Instruments of the same type present a

good or very good agreement. However, bias does not quantify event by event agreement does

not show how POSS disdrometers recorded less rain than other type instruments in most of the

events (see Figure 3.9).



bias (%) gauge1 JW1 JW2 Parsivel1 Parsivel2 POSS1 POSS2

gauge1 - 1,82 12,73 14,42 5,73 19,63 25,09

JW1 1,82 - 11,11 12,83 3,98 18,14 23,70

JW2 12,73 11,11 - 1,94 7,42 7,91 14,17

Parsivel1 14,42 12,83 1,94 - 9,22 6,09 12,46

Parsivel2 5,73 3,98 7,42 9,22 - 14,74 20,53

POSS1 19,63 18,14 7,91 6,09 14,74 - 6,79

POSS2 25,09 23,70 14,17 12,46 20,53 6,79 -

Excellent bias<5%

Very good 5% < bias < 10%

Good 10% < bias < 15%

Reasonable 15% < bias < 20%

Not acceptable bias > 20%

Table 3.2.- Bias of total accumulations between different instruments.

Let’s analyze these performances in more detail from an event by event perspective in order to

find if the trends shown by the bias are systematic.

Figure 3.9 shows comparison on accumulations of the single events for each disdrometer with

the gauge, and between instruments of the same type. We use the following statistics in order

to evaluate the results, considering x1, x2 the variables that contain the events accumulations

of one instrument.

• Standard deviation of rain total differences; represents the standard deviation of the

absolute error on the estimation of one event.

SDRTD = Var(x1 x2)( )1/ 2

= Var(x1) +Var(x2) 2Cov(x1,x2)( )1/ 2

(3.5)

• Pearson correlation coefficient:

r =Cov x1,x2( )

Var(x1) Var(x2)( )1/ 2 (3.6)

• Weighted mean absolute rain total difference, percent error on the estimation of one

event weighted to give more importance to the biggest events, taking as a reference one

of the instruments.

< R >= w1i(x1i x2i)

x1ii

N

100 < R >= w2i(x1i x2i)

x2ii

N

100 (3.7)

where the weights are:

w1i =x1i

x1ii

N and w2i =x2i

x2ii

N(3.8)

Figure 3.10 shows an excellent consistency between gauge measurements. Moreover JW1 and

Parsivel2 recorded every single event presented a good agreement with the gauge, with good



correlation coefficients 0.998 and 0.997 and small weighted mean rain total differences 6.6 %,

6.7 % and 8.0 %, 8.4 % respectively. These results allow us to consider the gauge data as

nearly the truth; this is an important point in this study, because we have found a criterion to

evaluate the different instruments, in terms of rain accumulation.

Although this excellent agreement between the gauge and JW1 there is one outliner where this

disdrometer recorded much less rain than the gauge; it is worth noting that JW2 also presented

its worst performance in this case (event #5). In the following sections (3.4, 3.5) this event is

going to be analyzed. Although in some events JW1 recorded more rain than the gauges the

less total accumulation is due to some high events where it took slightly less rain. Meanwhile

Parsivel2 is systematically recording less than gauges. However it presented a more consistent

behavior.

The other optical instrument presented a systematic underestimation of rainfall, with an

excellent correlation with the gauge but higher <| R|>. In spite of the good recording of the

JW2 in most of the events, differences on the events that record more rainfall and the

previously mentioned outliner (event #5) makes increase the standard deviation for this

disdrometer.

While impact (JWD) and optic (Parsivel) instruments had at least a good agreement with

gauge, both POSSs measures had high differences with standard deviation of rain totals over

10 mm. POSS disdrometers underestimated most of the events, although there were two events

where they presented the opposite behavior (events #2 and #3). These two events are analyzed

detailed in the following sections.

Continuing with the same analysis, and taking care of performances between instruments of

the same type we found a systematic behavior between them: JW1, Parsivel2 and POSS1

recorded more rain total than JW2, Parsivel1 and POSS2 respectively in almost everyt event,

with standard deviation of rain totals round 3 mm for all them.



1 10 100Gauge1

1

10

100G

auge2 (

mm

)

SDRTD = 0.628 mmCorrelation = 1.000<|∆R|> = 2.1% ; 2.1%

1 10 100Gauge1 (mm)

1

10

100

JW1 (

mm

)


1 10 100Gauge1 (mm)

1

10

100

JW2 (

mm

)


1 10 100Gauge1 (mm)

1

10

100

Pars

ivel1

(m

m)


1 10 100Gauge1 (mm)

1

10

100

Pars

ivel2

(m

m)


1 10 100Gauge1 (mm)

1

10

100

PO

SS

1 (

mm

)


1 10 100Gauge1 (mm)

1

10

100

PO

SS

2 (

mm

)


1 10 100JW1 (mm)

1

10

100

JW2 (

mm

)


1 10 100Parsivel1 (mm)

1

10

100

Pars

ivel2

(m

m)


1 10 100POSS1 (mm)

1

10

100

PO

SS

2 (

mm

)


Figure 3.10.-Comparison of event rain totals

3.4 Raindrop Size Distribution

Comparison of the disdrometers composite DSD (equation (3.9)) of all rain events provided a

deeper look on the performance of individual disdrometers (Figure 3.11).

composite DSD =

DSDt

t=1

k

k

(3.9)



The agreement between the two JWD was excellent except at the two extrems of the spectrum

where JWD1 recorded more raindrops. Similarly, the agreement between the two POSS and

between the Parsivel was excellent except Parsivel2 had recorded more raindrops over 3.5 mm

diameter. The agreement between Parsivel and JWD was also very good except at sizes less

than 1.2 mm diameter where Parsivel was first recorded more rainfall toward small sizes, but

then had a sharp drop off at diameters less than 0.5 mm diameter. This indicates that Parsivel

is not reliable at small drops less than 0.5 mm diameter. Parsivel also measured a very low

percent of very large drops beyond JWD maximum diameter of 5.1 mm diameter. These very

large drops may have an effect on rainfall and reflectivity of the instantaneous observations,

but they do not play a significant role in event rain total due to the very rare occurrence.

POSS, on the other hand, had a very good agreement with JWD and Parsivel in the range

between 1.2-2.8 mm diameter, but recorded much more small and much less large and very

large drops than the other two types of disdrometers. The agreement or disagreement between

the overall composite spectra of the disdrometers could be result of an art-effect of averaging,

therefore, we will examine the event composite spectra of the individual units to further

evaluate the performance of the disdrometers.

Figure 3.11.-Composite DSD for each disdrometer during the whole fieldcampaign.

3.4.1 The performance of Joss-Waldvogel disdrometers

The composite raindrop spectra of the rain events showed a good agreement between the two

impact disdrometers except in 30% of the cases where noticeable differences were observed in

mid-size and large drop counts (Figure A.1 and Figure A.2). JWD1 recorded more mid-size

and large drops than JWD2 in these cases resulting in at least 15% more rainfall in JWD1. The

mid-size drops are mainly responsible for the differences in rain rate between the two JWD

since they contribute over 75% of the rainfall.

3.4.1.1 Contribution of different drop sizes to the rain rate

On a case-by-case basis, small or large drops had significant contribution to measured rainfall.

The small drops represent over 10% of the rainfall in one-third of the rain events, in these



events the contribution of large drops was 3% or less in JWD1. These are the narrow spectra

events where the maximum drop diameter was 3.5 mm or less.

The large drops represent over 10% of the rainfall in half of the rain events, coinciding the

contribution of small drops 10% or less in JWD1.

Very large drops (drops that fall into the largest drop size interval of JWD), record rainfall

nearly half of the rain events, but they represent at most 4% of rainfall in JWD1.

Similar contributions to the rainfall of the four ranges of raindrop (that is, very small, mid-

size, large and very large drops) were found in JWD2.

3.4.1.2 Contribution of different drop sizes to the reflectivity

The mid-size drops contributed over 50% to radar reflectivity in 2/3 of the rain events in

JWD1.

The small drops represents at most 3% of the reflectivity in a rain event, except four events

with narrow spectra where the contribution was at most 16%.

Since the radar reflectivity is proportional to the sixth moment of drop diameter, the large and

very large drops contributed more than 50% of radar reflectivity in remaining 1/3 of the rain

events. The very large drops alone had more than 10% contribution to the reflectivity in four

events.

Similar contributions of four raindrop size ranges to reflectivity were found in JWD2.

3.4.1.3 Differences between both instruments

A key question is the reasoning of the differences in the drop concentration of mid-size and

large drops between the two JWD. Since the differences in drop counts was observed in some

but not all the events, we considered three possible causes.

• Sampling differences are mainly attributed to low drop counts of very large drops and

large drops in the presence of relatively narrow spectra. Here, the differences in drop

counts also occurred in mid-size drops where the concentration of mid-size drops was

as high as 2000 in cubic meter, meaning no dependency to the sampling.

• Meteorological factors such as wind or turbulence could also be the reason of

differences of drop counts between the two JWD. Since we had only wind

measurements in a nearby station, we cannot pinpoint about this factor as a cause. Our

analysis showed that significant differences in drop concentration occurred at both

relatively low and high mean wind speeds.

• An interesting factor is that JWD1 had more mid-size and large drops than JWD2 in

the events where significant differences occurred between the two spectra. We first

considered a possible calibration factor that may also cause for the differences, but we

rule out it since the calibration differences cannot explain differences over 3-4% in

rainfall and not in most of the cases showed systematic differences in mid-size and

large drops between the two disdrometers.

3.4.1.4 Performance on a case-by-case basis

To demonstrate the performance of JWD on a case-by-case basis, the event composite spectra

of Parsivel2 was added to the corresponding event composite spectra of JWD1 and JWD2 in



Figure A.1 and Figure A.2. While the agreement between Parsivel2 and both JWD is quite

good in mid-size and large drops, Parsivel2 recorded considerable more drops in the range 0.5

to 1.5 mm in most of the events. Parsivel2 had also more very large drops than JWD, but the

rate occurrence of these very large drops had a rather small contribution to reflectivity and no

contribution to rainfall.

As noted in the previous section, JWD1 had a very good agreement with collocated gauge rain

totals except for event #5 where JWD1 recorded 4.4 mm less rainfall, in the same way the

other JWD showed also his worst performance recording 5.21 mm less. The composite spectra

of JWD showed a concave down shape at small drops only in this event where maximum drop

concentration occurred at 1.1 mm in diameter. Raindrop spectrum in event #5 was narrow with

maximum drop diameter of 3.5 mm in diameter where the medium size drops had the main

contributors to both rainfall and reflectivity, but also where the small drops took an important

part on the total accumulation. Thus, differences on the first bins, as is the case, became

particularly important to the rain estimation.

Event #5 was a windy case with the highest median wind speed (16 m/s). Assuming that wind-

induced background noise suppressed the small drops, we artificially extend the slope of the

distribution down to the threshold of the minimum measurable drop size of the JWD. The

recalculated rainfall of JWD reduced the disagreement between gauge and JWD (1.6 mm

differences for JWD1), but still had more drops at sizes below 1.5 mm. This exceptional case

illustrates the importance of the presence of other type disdrometers in field operation.

Among the other rain events, the JWD1 recorded 2.7 mm less rain than the collocated gauge in

event #3. This was a heavy rain event with wide raindrop spectrum. Large drops had a

significant contribution to accumulated rain and played a major role in reflectivity. It was also

the second windiest event with median wind speed of 14 m/s. The composite spectra of

Parsivel2 had more small and large drops than JWD. In this event, the lack of small drops due

to the noise of wind and also to the dead time effect (which appears only in heavy rainy

minutes), because of the wide spectra, did not have the same influence in the accumulation

than in event #5.

While the differences of spectra of Parsivel and JWD shined a light in determining the event

rain total differences between JWD and gauges in event #3 and previous events, the presence

of more drops over 2.5 mm in Parsivel2 than JWD spectra in event #27 did not resulted in

more rainfall in Parsivel2. This is mainly due to the fact that the raindrops were falling at

lower velocities than their proposed fall velocity at mid-size drops that had a significant

contribution to rainfall.

If we assume that raindrops were falling at the proposed fall speed, Parsivel2 records less mid-

size drops at 1.5-2.5 mm diameter range than the JWD1, compensating the relatively higher

rainfall in Parsivel2 from the raindrops over 2.5 mm, that does not have the same contribution

to the rainfall.

3.4.2 The performance of Parsivel disdrometers

The event composite spectra of the two Parsivel instruments showed a very good agreement

for small and mid-size drops, while one of the Parsivel had more large drops than the other in

1/3 of the rain events (Figure A.3 and Figure A.4).



In this study, we employed the measured fall speed of Parsivel and the differences in fall

speeds could result in differences in raindrop concentration. The mean fall velocity of the

drops in Parsivel2 was relatively higher than in Parsivel1 at large drops. Since the velocity is

in the denominator in the calculation of concentration, Parisvel2 recorded more large drops

than Parsivel1 when their composite spectra show no difference.

On the other hand, rainfall is directly calculated from drop counts without requiring the

knowledge of fall speeds (equation (3.2)). As a result, there was no one-to-one match between

the events where one of the Parsivel had more large drops and the differences in rainfall

between the two Parsivel were relatively high. However, for the events where Parsivel2

composite spectra had higher concentration (Figure A.3 and Figure A.4) the rainfall was also

at least 10% higher in Parsivel2 than in Parsivel1.


Mid-size drops were the main contributors to the rain accumulation except for four events

where most of the contribution came from small drops in Parsivel1. These four events had

narrow spectra as expected.

The small raindrops contributed more than 10% of the rain accumulation in nearly 2/3 of the

rain events where the contribution of large drops were less than 10% except in two events. The

contribution of small and large drops to rainfall ranged between 14 and 18% in these two rain

events.

Large drops contributed more than 10% of the rainfall in the remaining 1/3 of the events.

There were four events where maximum drop diameter was at most 3 mm diameter (no large

drops). The very large drops were only recorded over 40% of the events, but their contribution

to the rain rate was, at most, 3%.

Similar contribution of the four drop-size ranges to rainfall was found in Parsivel2 except very

large drops were found nearly in half of the rain events.


Mid-size drops were the main contributors to reflectivity in over 2/3 of the rain events, while

large and very large drops most contributed in reflectivity in the remaining 1/3 of the rain

events in Parsivel1.

The very large drops alone contributed to the over 10% of the reflectivity in nearly 1/3 of the

rain events, while small drops contributed over 10% of its reflectivity in one event.

3.4.2.3 Main drop detection problems

Based on previous measurements (Beard et al. 1986), we considered the maximum drop

diameter for any size distribution as 8.0 mm. However, we observed 8 drops where their

diameters were above 8.0 mm with a maximum drop diameter of 11.3 mm. In the absence of

any hail report, we consider this drops as spurious and they were eliminated from observed

spectra.

Parsivel is not reliable for the drops less than 0.5 mm diameter as reported above: these

disdrometers recorded a great amount of drops at that range. A possible explanation is the

presence of some drops that could have splashed on the tunnel housing in spite of the anti-

splash protection.



In regard to the rain accumulation, Parsivel2 was in very good agreement with tipping bucket

gauges, and presented a consistent behaviour recording in almost all the events slightly less

than the gauges, but with no outliers, nor different behaviour with different kind of events, nor

with different behaviour in the wind cases. There is no clear reason to the explain differences

between both optical disdrometers.

In spite of the underestimation on the calculation of the fall velocities, these measures allow us

to evaluate how it changes due to the horizontal winds. Thus, Figure 3.12 shows mean fall

velocities for two selected events with different wind regime. There is a clear difference in

midsize and large drops where, in the windy event fall at lower velocities. This is an important

point to consider on the JW and POSS disdrometers, which consider drops falling at a fixed

fall velocity.

Figure 3.12.-Mean fall velocity for a windy and non-windy events in comparisonwith the mean fall velocity for Parsivel2

3.4.3 The performance of POSS disdrometers

The event composite spectra of two POSS showed an excellent agreement between them

(Figure A.5 and Figure A.6). However, the slight differences in small and mid-size drops in

half of the events resulted in at least 10% more rainfall in POSS8.


Some differences occurred in the measurements of big drops, but their contribution in the

accumulation was small, and only in one event these drops represented more than 10% of the

accumulation (the only case where the disagreement in this range of diameters represented

more than 10% of the total differences).

In both POSS very small drops represented more than 10% in nine events of the rain totals,

and mid-size drops represented in 90 % of the events over 50 % of the rainfall, the rest three



events very small drops presented over 20 % (these were the events with the narrower

spectra).

Small drops take more importance in some events not only in terms of percentage as well as in

rain accumulation: while drops up to 0.6 mm diameter represented 20.9 and 17.4 mm of rain

accumulation for radar disdrometers Parsivel2 recorded 4.6 mm. This tendency is opposed in

big drops: while POSS1 and POSS2 took 29.1 and 29.39 mm Parsivel2 recorded 104.32 mm.


Considering radar reflectivity, large drops had less importance than in the other instruments.

Only in one event, they contribute more than 30% in the reflectivity while this percentage was

higher in 14 events for Parsivel2.

3.4.3.3 Main drop detection problems

To clarify the performance of these POSS disdrometers, as we did for impact type

disdrometers, the event composite spectra of Parsivel2 is added (Figure A.5 and Figure A.6).

In all the events both radar disdrometers recorded much more very small drops despite the

imposed cap of 10.000 drops/m3, and recorded much less large drops when the composite

spectra was wide. In three events both radar disdrometers captured much more mid-size drops,

those where these instruments recorded much more rainfall than the gauges.

Figure A.5 and Figure A.6 show three different tendencies on the performance of POSS.

• Events with narrow spectra present a good agreement with the spectra of the Parsivel2,

except in the very first bins where POSS recorded more very small drops. This type of

event represented 40% of the events. The slight differences between both POSS in

small and mid-size drops in these events had more importance in percentage

differences on the rain accumulation and were the main cause of the differences

between both instruments. These were the events with the better agreement with the

other instruments in terms of rain accumulations.

• Events with wide spectra, which represent 50% of the events, where both radar

disdrometers underestimated the drops in all the range of diameters.

• Three extremely windy events with wide spectra were both instruments observed more

drops up to 2.5 mm and much less for bigger diameters, resulting in overestimated rain

rates in comparison with the rest of the instruments.

There were also windy events (for example events #8, #11, #14, #20, #22, #27, #30) where

both POSS recorded less rain than the other instruments. In these events the wind influence is

absorbed and compensate (as a consequence of the wind location in the event or its duration)

by the rest of non-windy minutes.

3.5 Event-by-event analysis

Comparison of the disdrometer composite DSD event by event has provided an important

sight on the instrument performances. However, these characteristics could result an effect of

averaging. In order to distinguish more accurately the different performance of the instruments

here we analyze time series minute by minute of rain intensity, wind data and DSD for one

disdrometer of each for six selected events (note that the colour scale is represented in

logarithmic scale). Composite DSD of selected events are presented in Figure 3.13.



0 82 4 6Diameter (mm)

10 4

10 2

10 0

10-2

10-4

Co

nce

ntr

atio

n (m

-3m

m-1

)a) b)

c) d)


10 4

10 2

10 0

10-2

10-4

Co

nce

ntr

atio

n (m

-3m

m-1

)


10 4

10 2

10 0

10-2

10-4

Co

nce

ntr

atio

n (m

-3m

m-1

)


10 4

10 2

10 0

10-2

10-4

Co

nce

ntr

atio

n (m

-3m

m-1

)

JW1Parsivel2POSS1

event #3 event #5

event #11 event #20

e)


10 4

10 2

10 0

10-2

10-4

Co

nce

ntr

atio

n (m

-3m

m-1

) event #27

Figure 3.13.-Composite DSD of the selected events for JW1, Parsivel2, POSS1



0

10

20

30

40

50

win

d (m

/s)

1

2

3

4

5

6

D (

mm

)

1

2

3

4

5

6

D (

mm

)

06:0004:00 04:30 05:00 05:30time (local hour)

1

2

3

4

5

6

D (

mm

)

0

200

400

600

inte

nsity

(m

m/h

)

10 4

10 -2

10 -1

10 0

10 1

10 2

10 3

Con

cent

ratio

n (m

-3m

m-1

)

JW 1

Parsivel 2

POSS 1

36.5 m/s

Mean wind speed 13.9 m/s

Maximum wind speed

a)

b)

c)

e)

d)

POSS1Parsivel2JWD

41.81 mm15.84 mm13.57 mm

Gauge 16.26 mm

Accumulated rain

Figure 3.14.-a) Rainfall time series b) Horizontal wind time series. c,d,e) DSD timeseries for JW1 Parsievel2 and POSS1 respectively for event # 3.

Event # 3: This was a short event, with heavy rainy minutes and strong horizontal winds

(Figure 3.14).

• JW1: recorded less small drops due to the heavy winds and the dead time effect present

on heavy rainy minutes.

• Parsivel2: Less small drops and presence of low concentration of very large drops.

• POSS1: Spurious small and mid-size drops and less large drops. Missing heavy rain

due to lightning.



10

20

30

40

50

inte

nsity

(m

m/h

)

10

20

30

40

50

win

d (m

/s)

1

2

3

4

5

6

D (

mm

)

1

2

3

4

5

6

D (

mm

)

22 23 24 25 26

time (local hour)

1

2

3

4

5

6

D (

mm

)

27

JWD

10 4

10 -2

10 -1

10 0

10 1

10 2

10 3

Con

cent

ratio

n (m

-3m

m-1

)

24.0 m/s


Maximum wind speed

JW 1

Parsivel 2

POSS 1

a)

b)

c)

e)

d)

POSS1

Parsivel2

9.43 mm

8.51 mm

4.00 mm

Gauge 8.13 mm

Accumulated rain

Figure 3.15.- a) Rainfall time series b) Horizontal wind time series. c,d,e) DSDtime series for JW1 Parsievel2 and POSS1 respectively for event # 5.

Event #5 There were strong horizontal winds during the whole event (Figure 3.15).

• JW1: Much less small drops mainly due to continuous windy conditions, this effect

produced the instrument to fain in the measuring the drop spectra when it was

extremely narrow.

• Parsivel2: Less small drops but excellent agreement with other disdrometers in mid-

size and large drops.

• POSS1: Spurious small drops mainly during the later phase of the storm. Here the wind

does not affect mid-size drops measurements because of the light intensities. It missed

some rainy minutes.



10 4

10 -2

10 -1

10 0

10 1

10 2

10 3

Con

cent

ratio

n (m

-3m

m-1

)

0

50

100

150

200

250

inte

nsity

(m

m/h

)

10

20

30

40

50

win

d (m

/s)

1

2

3

4

5

6

D (

mm

)

1

2

3

4

5

6

D (

mm

)

3 4 5 6 7 8 9

time (local hour)

1

2

3

4

5

6

D (

mm

)

POSS1

Parsivel2

JWD

47.3 mm

68.52 mm

62.46 mm

Gauge 67.06 mm

14.7 m/s


Maximum wind speed

JW 1

Parsivel 2

POSS 1

a)

b)

c)

e)

d)

Accumulated rain


Event # 11 This was an event with two different regimes (Figure 3.16).

• JW1: Missing small drops during the first segment of the event because of the

background noise induced by wind or the dead time effect.

• Parsivel2: The number of small drops was underestimated but the instrument was able

to capture low concentration of very large drops.

• POSS1: Missing rainy minutes in heavy rain, recording much less large drops. When

the horizontal wind was light underestimated all the spectra.



0

50

100

150

200

inte

nsity

(m

m/h

)

0

10

20

30

40

50

win

d (m

/s)

1

2

3

4

5

6

D (

mm

)

1

2

3

4

5

6

D (

mm

)

1912 13 14 15 16 17 18time (local hour)

1

2

3

4

5

6

D (

mm

)

POSS1

Parsivel2

JWD

63.20 mm

104.17 mm

104.06 mm

Gauge 105.16 mm

10 4

10 -2

10 -1

10 0

10 1

10 2

10 3

Con

cent

ratio

n (m

-3m

m-1

)

15.5 m/s


Maximum wind speed

JW 1

Parsivel 2

POSS 1

a)

b)

c)

e)

d)

Accumulated rain


Event # 20: This was a long event with very high intensities and wide spectra mostly with low

or moderate horizontal winds. (Figure 3.17)

• JW1: Slight underestimation of small drops especially in heavy rainy minutes.

• Parsivel2: The number of small drops was underestimated but the instrument had a

very good agreement with impact disdrometers in mid-size and large drops.

• POSS1: General underestimation of the drop spectra, especially for small and large

drops.



10 4

10 -2

10 -1

10 0

10 1

10 2

10 3

Con

cent

ratio

n (m

-3m

m-1

)

0

20

40

60

80

100

120

inte

nsity

(m

m/h

)

0

10

20

30

40

50

win

d (m

/s)

1

2

3

4

5

6

D (

mm

)

12 14 16 18 20 22 24time (local hour)

1

2

3

4

5

6

D (

mm

)

1

2

3

4

5

6

D (

mm

)

POSS1

Parsivel2

JWD

63.09 mm

70.06 mm

73.00 mm

Gauge 76.71 mm

20.7 m/s


Maximum wind speed

JW 1

Parsivel 2

POSS 1

a)

b)

c)

e)

d)

Accumulated rain

Figure 3.18.-a) a) Rainfall time series b) Horizontal wind time series. c,d,e) DSDtime series for JW1 Parsievel2 and POSS1 respectively for event # 27.

Event # 27: This was a long event with high intensities (Figure 3.18).

• JW1: Light underestimation of small drops due to wind and the dead time effect in

heavy rainy minutes.

• Parsivel2: The number of small drops was underestimated but overestimated the mid-

size and large drops than the other disdrometers in some minutes. This instrument

detected simultaneous drops.

• POSS1: Underestimation of small drops during the early stage of the storm and

overestimation of theses drops in the later part of the event (the rest of the narrow

spectra was recorded correctly).



3.6 Conclusions

This chapter focused on the analyses of the disdrometric data collected through a field

campaign during May to August 2004 at NASA Wallops Island Facility. Our main objective

was to evaluate the performance of different disdrometers. The measurements of impact, optic

and radar type disdrometers were compared and validated against two tipping bucket rain

gauges. Agreements and disagreements were discussed through 30 major rain events.

In terms of rainfall accumulation it resulted an excellent performance between both tipping

bucket gauges event by events. Two disdrometers (JW1 and Parsivel2) presented very good

agreements with the gauges. These results allowed us to take the gauges as a reference in

terms of rain accumulation. Meanwhile the other JWD and Parsivel resulted in a good

agreement with the gauges. Both POSS disdrometer showed a poor agreement with the gauges

mostly underestimating the rate but also in some events with the opposite behaviour. Between

same type of instruments, one of them recorded almost systematically more than the other.

In terms of composite DSD we found slight differences between same types of instruments.

Considering different disdrometers, POSS instruments presented poor agreements with the

rest, recording extremely less large and very large hydrometeors and considerably more small

drops. Parsivel instruments showed no reliability for drops less than 0.5 mm diameter. These

composite DSD showed the impossibility to record very large drops for both JWD and POSS.

Analyzing minute-by-minute spectra event by event we point out the following conclusions

regarding to the different instruments performances:

• Impact disdrometers presented problems in the record of small drops due to two

different reasons that in some cases were added.

o Because of the presence of strong horizontal winds.

o Because of the dead time effect in heavy rainy minutes.

• Impact disdrometers were unable to determine the size of very large drops.

• Optical disdrometers had the most reliable behaviour particularly in windy conditions

but were unable to record correctly drops less than 0.5 mm diameter. It presented

minutes with simultaneous drop detection.

• Radar had important problems when strong horizontal wind recording an unusual

number of small and mid-size drops (this is particularly important in minutes with wide

spectrum and the were the overestimation is extended to mid-size drops).

• Radar disdrometers presented a good agreement in drop spectra when narrow DSD

except in the concentration of small drops, which is overestimated because of the

presence of strong horizontal winds or underestimated when the absence of wind.

• When deep convective rainfall and weak horizontal winds radar disdrometers

underestimated the entire spectrum.

• Radar disdrometers did not operate up to four consecutive minutes during heavy rain in

the presence of lightning.

3 multi-sensor measurements of raindrop size distribution...

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