# A New Videosonde with a Particle Charge Measurement Device for In Situ Observation of Precipitation Particles

M.-P. Boussaton, S. Coquillat, S. Chauzy, F. Gangneron

► To cite this version:

M.-P. Boussaton, S. Coquillat, S. Chauzy, F. Gangneron. A New Videosonde with a Particle Charge Measurement Device for In Situ Observation of Precipitation Particles. Journal of Atmospheric and Oceanic Technology, 2004, 21, pp.1519-1531. 10.1175/1520-0426(2004)0212.0.CO;2. hal-00136321

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# A New Videosonde with a Particle Charge Measurement Device for In Situ Observation of Precipitation Particles

M. P. BOUSSATON, S. COQUILLAT, S. CHAUZY, AND F. GANGNERON

Laboratoire d'Aérologie, Observatoire Midi-Pyrénées, Toulouse, France

(Manuscript received 1 December 2003, in final form 26 March 2004)

## ABSTRACT

A new videosonde designed for microphysical soundings inside thunderclouds is described. This sensor makes use of a charge-coupled device (CCD) camera and can provide the phase (liquid or ice), the shape, the size, and the electric charge of each precipitating particle detected singly, the size of which ranges from 0.5 mm to 2 cm and the electric charge from ±1 to ±400 pC. The performances of the videosonde are analyzed and evaluated. It is found that the accuracy on the size measurement varies between about 13% for the smaller sizes and less than 2.6% for the larger sizes; meanwhile the average accuracy on the charge measurement is 3.2%. The determination of several large-scale parameters deduced from the videosonde data and comparable with radar observations and electrical soundings is presented. An intercomparison experiment with a disdrometer at the ground shows that the size distribution is perfectly restituted for large drops, even though the video permits filming at a maximum rate of only 50 images per second. The discrepancies that appear for smaller sizes are probably due to windy conditions to which small particles are much more sensitive, but it does not affect the rainfall-rate determination.

## 1. Introduction

While the development of mesoscale models requires a reliable parameterization of electrical phenomenon, several questions on discharge triggering and electrification processes in thunderclouds remain open at the present time. We still do not know which particles, with which electric charge, in which intense electric field, in which volume of intense electric field, are responsible for cloud discharges. Blyth et al. (1998) showed that glancing collisions involving at least one raindrop can produce long liquid filaments at the tips of which corona—the first step to a propagative discharge—is readily emitted in electric fields down to 150 kV m⁻¹. Furthermore, numerical results by Coquillat and Chauzy (1994) and Georgis et al. (1995) indicate that corona can also be triggered at realistic altitudes from stable raindrops as long as their electric charge is high, or pairs of drops interact. On the other hand, an alternative mechanism based on the acceleration of high-energy electrons produced by cosmic rays could lead to propagative discharges too (see Solomon et al. 2001). Further in situ observations of the microphysics involved in the high flash rate events are required for evaluating

the probability of occurrence of these various mechanisms.

With regard to thunderclouds electrification, laboratory experiments have determined various processes able to provoke charges separation inside clouds. One of them, called graupel-ice mechanism, appears to be capable of producing maximum electric field magnitudes, comparable to those observed in thunderstorms. However, as several factors implied in this process remain poorly understood and a vast number of mechanisms are able to electrify clouds, a definitive explanation is not available yet (MacGorman and Rust 1998). Type of particles and electric field intensities implied in the diverse electrification processes being different, supplementary in situ measurements would be helpful to settle among the existing hypotheses.

In order to observe the microphysical population of thunderclouds and to investigate the possible relationship between microphysics and lightning flash rate, we plan to perform in-cloud observations with a microphysical sensor.

Some instruments dedicated to in situ measurements have existed for several decades. Sensors such as 2D Particle Measuring Systems (PMS) probes, which are used in the case of aircraft observations, give very good results (Knollenberg 1970; Gaskell et al. 1978; Christian et al. 1980; Dye et al. 1983; Joe and List 1987; Cupal et al. 1989; Weinheimer et al. 1991; Lawson et al. 1998). However, these sensors, except the one of Murray et al.

<footnote>Corresponding author address: M. P. Boussaton, Laboratoire d'Aérologie, Observatoire Midi-Pyrénées, UMR CNRS/UPS N°5560, 14 avenue Edouard Belin, 31400 Toulouse, France.
E-mail: boum@aero.obs-mip.fr</footnote>
<figure><img src="image_2.png" /><figcaption>FIG. 1. Picture of the microphysics sensor.</figcaption></figure>

(1999), are not designed to measure particles charge. Furthermore, they are rather complex and expensive and require an aircraft logistic. In this way, we chose to perform balloon-borne soundings that are safer and have the advantage to provide vertical profiles even though the sampling volume is unavoidably smaller.

Marshall and Winn (1982) performed a balloon-borne instrument detecting particles charge and deducing particles size from fall speed measurement. A drawback of this system is that uncharged particles are not observed. Bateman et al. (1994) improved this kind of sensor with an optical measure of hydrometeor size. However, particle shape and phase are still unknown, whereas this information is of importance for determining the incloud electrification processes and the relationships between microphysics and lightning activity. Several Japanese researchers solved this problem using video sensors as an optical system. A video sonde was first developed by Murakami et al. (1987) and Murakami and Matsuo (1990). Dedicated to cloud particle observation, this sensor takes pictures of hydrometeors as they fall on a film dish. It is impossible though to detect their electric charge. Takahashi (1990) designed a sensor that is the only one capable of simultaneously measuring particle charge, size, shape, and phase. The only disadvantage seems to be the high cost and the low sampling volume of the apparatus.

Given the above characteristics of the various balloon-borne sensors, we designed a new instrument that provides the phase, the size, and the electric charge of precipitation particles. The technology used is simplified in order to reduce the costs since the sensor is to be lost after each sounding. It is inspired by the existing microphysics sensors (Bateman et al. 1994; Takahashi 1990; Takahashi et al. 1995, 1999). Precipitating particles are directly observed via a low-priced charge-

coupled device (CCD) video camera, an induction ring is used in order to measure particle charge, and data are transmitted to the ground by telemetry. The data processing permits the determination of various parameters that are also of importance for the comparison with radar and model data: precipitation rate *R*, dimensional distribution *N(D)*, electric charge density *ρ*, and radar reflectivity factor *Z*. The precipitation rate calculation has been tested at the ground from the comparison between data from the video sonde and from a disdrometer.

## 2. The video sonde

The video sonde necessary to realize in situ observations is duty bound to satisfy some requirements: it must be able to detect particle phase, shape, size, and charge inside a thundercloud while being relatively light (in order to be associated with other measuring instruments) and less expensive (lost after sounding). It has been designed in order to fulfill these conditions. In particular, it is dimensioned to detect the particles singly with sizes ranging from about 0.5 mm to more than 2 cm and charges ranging from ±1 to ±400 pC.

A picture of the microphysics sensor is displayed in Fig. 1. This sensor presents a double entrance destined to reject particles whose trajectory would be too tilted (see Figs. 1 and 2). Particles that strike the primary entrance or whose trajectory is too tilted are drained off. The other particles enter an induction cylinder intended to measure their electric charge. Below is the observation volume that comprises a CCD video camera and a screen. The use of CCD sensor rather than photodiode array permits us to get the whole shape and the phase of each particle. The camera field of view has an angle of about 50° in the vertical plane and 68° in the horizontal plane (Fig. 3). A screen is situated at the back
<figure><img src="image_3.png" /><figcaption>FIG. 2. Vertical section of the videosonde.</figcaption></figure>

of the camera field of view. A light emitting diode (LED) array is inserted in the right side of the screen (see pictures in Figs. 4 and 13 later) in order to display the electric charge and its sign in binary code. The video camera continually films at a rate of 25 images per second. The deinterlacement of the pictures in the subsequent data processing allows it to reach 50 images per second. The shutter speed of the CCD camera is not controlled. Actually it ranges from 1/100 000 to 1/50 s and is automatically adjusted as a function of the illumination. Two halogenous lamps tested in a preliminary experiment ensure a sufficient illumination to reduce the camera time of aperture in order to get sharp pictures for the largest particles that have the highest fall velocities, taking into account the rate of ascent of the balloon (5 m s⁻¹). These lamps also allow the determination of the particle size via the location of the induced shadows on the screen. For maximizing the lighting and avoiding any image saturation by undesirable reflection, the back screen is covered with white diffusing plastic finely sandpapered. We gave up the use of an optical barrier for triggering the camera because,

<figure><img src="image_4.png" /><figcaption>FIG. 3. Horizontal section of the videosonde.</figcaption></figure>

first, the system would have been much more expensive, and second, the response time of the camera (40 ms) is too long compared to the transit time of the particles in the sensor (about 10 ms). The observation volume dimensions are a compromise between a little distance to the camera and a large field depth. So as to gain observable volume, the dimension perpendicular to the optical axis has been increased so that the sensor section is an ellipse. Finally, the observable volume is superior to 53 cm³. It is greater than the volume of the videosonde developed by Takahashi (8.7 cm³); consequently, the size spectrum of observable particles is shifted toward larger sizes. Although the sensor volume is relatively important, it is little enough to make improbable the simultaneous presence of several particles, and thus, to avoid the cases in which a charge could not be attributed to the different particles present on the same picture. To justify this point, we can apply the calculation made by Bateman et al. (1994) for water drops: we consider a Marshall and Palmer distribution integrated between diameters of 0.3 and 6 mm (validity threshold of the Marshall-Palmer distribution) for a high precipitation rate of 100 mm h⁻¹. Then, the particle density is $n = 3215 \text{ m}^{-3}$, and the number of drops present at any time in the sensor volume is about 0.35, that is, largely smaller than 1. According to Auer (1972), who reported in the literature the available size distributions of graupels and hailstones (see Pruppacher and Klett 1978), the concentration of such particles is not higher than that of water drops in the same range of size. Therefore, the above conclusion on the maximum number of drops present at any time inside the video-
sonde appears valid for all kinds of precipitating particles.

The video signal that contains all the information is transmitted to ground by telemetry in real time via a video transmitter that has a frequency of around 405.5 MHz in the meteorological range 400–406 MHz. The receiver bandwidth is about 500 kHz. Such a limitation of the bandwidth does not directly affect the resolution of the video sonde since the transmitted signal is analog [Consultative Committee for International Radio (CCIR) standard, phase alternate lines (PALs) monochrome]. As a matter of fact, each frame is constituted of 625 scan lines and each line takes 64 µs to scan (25 frames per second, 40 ms per frame). The video signal is a sequence of analog signals corresponding to each line, separated by synchronization pulses. With this standard, the reduction of the bandwidth results in a smoothing of the continuous (time varying) signal of each line that loses high-frequency (HF) components. In practice, this loss of HF components leads to a reduction of the contrast of a given picture. The result is a more blurred picture, the contrast of which can be enhanced by subsequent processing in order to increase the sharpness of the hydrometeor and shadow outlines. However, with poor contrast, small hydrometeors may not be easily detected. The ground-receiving antenna is of Yagi type (18.15 dB) with a 6-m span and a 2° × 11° aperture angle at -3 dB. It is equipped with a 18-dB antenna amplifier in order to enhance the telemetry range up to about 30 km. Moreover, the motor driven antenna orientation is automatically controlled in real time via the GPS data provided by the radiosonde used in the sounding system. This device ensures the antenna orientation to be within the aperture angle. The total consumption, taking into account the camera (120 mA), the two lamps (2 × 420 mA), and the video emitter (100 mA), amounts to 1060 mA. The power is provided by 1.5-V lithium batteries that have a capacity of 2570 mAh under a total tension of 12 V. The sensor structure is made of a 0.24-mm-thick tin sheet. The electronics and the batteries are protected from low temperatures by a Styrofoam layer and are recovered with a plastic coat that waterproofs the sensor, and with an aluminium layer that ensures an electrical shielding. The total mass of the video sonde and the emitter is about 950 g. The French standard allows the balloon system to be flown without the use of a transponder provided that, on the one hand, its whole weight (balloon and payload) is lower than 4 kg (a so-called light balloon) and, on the other hand, no unit of the payload is heavier than 2 kg with a maximum mass per unit area lower than 13 g m⁻². The present video sonde fulfils these conditions with a maximum mass per unit area of about 9.5 g m⁻². The entire instrument without the emitter is 29 cm long, 12 cm wide, and 20 cm high.

### 3. Size measurement

An individual hydrometeor size measurement is made using the hydrometeor picture extracted from the sound-

ing video film and saved on bitmap format (Fig. 4). Each distance between two selected points of the hydrometeor outline is determined from their polar coordinates with respect to the optical center.

The camera objective unavoidably induces an optical aberration. This aberration is purely radial: if a target composed of equidistant concentric circles is filmed, one can observe that the angles are preserved (circles are not distorted), but the radial distances are modified since the circles far from the center appear closer and closer to each other. A calibration made from the observation of such a target permits us to find a third-order polynomial adjustment between the apparent radius (pixels)

<figure><img src="image_5.png" /><figcaption>FIG. 4. (top) Picture of a large raindrop observed with the camera and sketch of the determination of the distance *l* between the observed drop and the camera based on the localization of the drop shadows.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIG. 5. Third-order polynomial adjustment of the real radius vs the apparent radius for the reference distances to the camera.</figcaption></figure>

and the real radius (cm) for a given distance between the target and the camera. This calibration has subsequently been performed for several camera-target distances—called reference distances—varying from 3 to 7 cm (back screen) every 0.5 cm (see Fig. 5).

Furthermore, the closer the hydrometeor is to the camera, the larger it appears. The determination of its real size requires therefore the knowledge of the distance between the hydrometeor and the camera. This distance $\ell$ is deduced from the real distance $d_0$ between both hydrometeor shadows present on the screen according to the following expression:

$$ \ell = L \frac{d_{\ell}}{d_{\ell} + d_{0}}, \qquad (1) $$

where L is the distance between the camera lens and the screen (7 cm) and $d_{\ell}$ is the distance between both lamp filaments.

In brief, the size determination requires five stages: 1) measurement of the apparent distance between both shadow centers, 2) calculation of the real distance $d_0$ between both shadow centers by using the polynomial adjustment corresponding to the camera-screen reference distance, 3) calculation of the camera-hydrometeor distance $\ell$ according to Eq. (1), 4) measurement of the apparent size of the hydrometeor, and 5) calculation of the real size by using a linear interpolation between the corrections corresponding to the reference distances that surround $\ell$.

Several parameters affect the precision on the particle

size measurement, rendering the theoretical calculation of the global uncertainty rather complicated. For simplicity, this calculation is not presented here. However, after a brief report of the potential sources of error, we present a simple calibration experiment that provides a good estimation of the size measurement uncertainty, which remains within the theoretical uncertainty range.

Two parameters do not vary: the distance L between the camera and the screen and the distance $d_{\ell}$ between both lamps. They are known with a 0.5-mm accuracy, leading to a relative uncertainty of about 0.7% and 2.2%, respectively. When analyzing the pictures, each apparent position is measured with a 1-to-3 pixels' accuracy depending on the sharpness of the picture and on the noise induced by the transmission line. Another source of error comes from the polynomial correction. The corresponding relative uncertainty on the real radius determination is deduced from the scattering of the data perceptible in Fig. 5. It is displayed for various reference distances in Fig. 6. For a given reference distance, it is maximum for the smallest radius, minimum for intermediate radii, and increases again for large radii. Furthermore, the shorter the reference distance, the larger the error that remains, however lower than 2% for the whole set of the calibration data.

The relative uncertainty on the distance $d_0$ that involves the polynomial correction remains around 10% for distances between shadows larger than 1 cm, but can be very high (up to 60%) for small distances $d_0$ (small particles close to the back screen). However, the
<figure><img src="image_7.png" /><figcaption>FIG. 6. Relative error on the determination of the real radius according to the polynomial correction.</figcaption></figure>

accuracy on $\ell$ is not sensitive to these extreme errors since, according to the error calculation deduced from Eq. (1),

$$ \frac{\Delta\ell}{\ell} = \frac{\Delta L}{L} + \frac{d_0}{d_\ell + d_0} \left( \frac{\Delta d_\ell}{d_\ell} + \frac{\Delta d_0}{d_0} \right), \quad (2) $$

the ratio in front of the parenthesis is much less than 1. Consequently, the average relative uncertainty on $\ell$ remains between 7.8% and 5.9% for particle sizes ranging from 1 to 8 mm.

The uncertainty on the determination of $d_0$ depends on the detection of the shadows. In some cases this procedure can be difficult to carry out, for example, 1) if a particle is too close from the screen and partly covers the shadows, 2) if a large particle is too close to the camera objective and covers the shadows, and 3) if a less solid particle like a snowflake gives blurred and fuzzy shadows. In the first case, the induced error on $\ell$ is negligible since the second right-hand-side term of Eq. (2) tends to zero (see above). The second case should not often occur except if the trajectory is too tilted. In cases 1 and 3, the particle size cannot be detected correctly and only qualitative information can be drawn.

In order to test the size measurement accuracy, we filmed three objects of different sizes (about 1, 3, and 8 mm), previously measured with a caliper square (precision of 1/50 mm) in various locations inside the field of view of the video sonde. This simple calibration experiment gave the following results displayed in Fig. 7. Contrary to the analogical optical apparatus like that of

Bateman et al. (1994), the measurement accuracy increases with particle size. The average relative uncertainty decreases from 12% to 2.5% for corresponding sizes ranging from 1 to 7.92 mm, respectively. As far as the overall range of size—detectable by the video sonde—is considered, the average accuracy may be estimated between about 13% and less than 2.5% for sizes ranging from 0.5 mm to 2 cm, according to Fig. 7. By comparison, the errors in size measurement by the sensor of Bateman et al. (1994) increases from 5% to 25% for size ranging from 1 to 6.4 mm.

## 4. Charge measurement

The electrical charge measurement is driven by the electronic system (Fig. 8). The electrical signals corresponding to marks A–E in Fig. 8 are displayed in Fig. 9. The passage of a charged particle through the electrode (induction cylinder of elliptical section) induces a bipolar current impulse (A). The integration of the current signal provides the temporal evolution of the induced charge inside the electrode (B). The induced offset is subsequently corrected and the charge signal is scaled to 2.5 V for 400 pC (C). The pulse polarity and duration are detected for the charge display (D). The peak value of the electric charge is then measured (E1) and displayed in binary code by the array of diodes, two of them being dedicated to the polarity, in the field of view of the camera (E2). This display is activated by a delayer so that it is synchronized with the particle passage through the camera field.
<figure><img src="image_8.png" /><figcaption>FIG. 7. Relative error on the size determination. The error bars correspond to the minimum and maximum error for each size.</figcaption></figure>

The measured charge ranges from ±1 to ±400 pC. The minimum threshold corresponds to the minimum current amplitude that can be detected by the electronics. It can reach 2 pC in some cases depending on the noise. It remains within the lowest range of charge that can be measured by various balloon-borne instruments (Marshall and Winn 1982; Weinheimer et al. 1991; Bateman et al. 1994). However, the video sonde of Takahashi et al. (1999) is the only one capable of realizing finer measurements of low charges (0.1 pC), but it is limited to 200 pC for high charges.

A necessary calibration experiment has been conducted with raindrops of known electric charge—falling along the central axis of the induction cylinder—in order to determine the relationship between the real and induced charges. However, Weinheimer (1988) has shown that the induced charge depends on the radial location of the particle in the cylinder. The closer the particle to the cylinder wall, the higher the induced charge. Consequently, an off-center trajectory could lead to an overestimation of the particle charge.

According to Weinheimer, the maximal error only de-

<figure><img src="image_9.png" /><figcaption>FIG. 9. Electrical signal corresponding to the marks A, B, C, D, and E in the electronics functioning described in Fig. 8.</figcaption></figure>

pends on the ratio of the cylinder half-length *c* over the radius *a*. Although present induction cylinder has an elliptic section, we can use Weinheimer's results to estimate the measuring uncertainty. Along the small axis, the ratio *c/a* is equal to 1. Figure 10, drawn for such a case, represents the quotient between induced charge *q*' and real charge *q* for different vertical (*z*₀/*c*) and radial (*r*₀/*a*) locations. We will consider the vertical location *z* = 0, midway between the cylinder inlet and outlet, where charge measurement is made. According to the sensor entrance geometry, the maximal distance from the center that can be reached by a particle in the cylinder corresponds to the ratio *r/a* = 0.6. Between the two radial locations characterized by *r/a* = 0 (center of the cylinder) and *r/a* = 0.6, the ratio *q*/*q* increases by 8.2%. This result implies that, in this case, the real charge of a particle falling through the cylinder is over-

<figure><img src="image_10.png" /><figcaption>FIG. 8. Schematic representation of the electronics functioning. Marks A, B, C, D, and E correspond to the electric signals referenced A, B, C, D, and E in Fig. 9.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>FIG. 10. Induced charge/real charge ratio for different locations inside the induction cylinder.</figcaption></figure>

estimated by 8.2% at the most. The same reasoning made in the perpendicular direction ($c/a = 0.71$) shows an overestimation reaching 25%. This extreme case does not, however, correspond to the most frequent events. These errors, specific to this kind of charge sensor, cannot be corrected since we are not able to know the particle position inside the induction electrode.

One can point out that the maximum error corresponds to the contact between the particle and the induction cylinder. In this case of tilted trajectories, the displayed charge overestimates the actual charge by a maximum amount of 25%. If this event concerns raindrops, the simultaneous splashing results in an increased number of small drops on the same image. The event is therefore rejected during the data analysis.

To check this theoretical measuring uncertainty due

to off-center trajectories, we performed a calibration experiment: drops of known charge were dropped inside the induction cylinder, along the central axis and as close as possible to the entrance wall in the direction of the larger axis of the ellipsoid section. The charges measured in both cases are plotted in absolute value in Fig. 11. As expected, the displayed charges appear slightly higher for off-center trajectories. However, the relative error displayed in Fig. 12 remains within 4%, which is rather low compared with the theoretical maximum error according to Weinheimer (1988). However, one can point out a relative error that can rise about 18% for the smaller charges (below 50 pC). This probably originates from the uncertainty on the charge produced by the drop generator that is higher for the lowest charges. This uncertainty is consequently enhanced by the logarithmic relationship between the actual and the displayed charge, which exhibits a higher slope in the case of small charges. The overall average accuracy is only 3.2%.

## 5. Achievable parameters

From the measurement of the individual characteristics (charge and size) of precipitation particles, we are able to calculate microphysical parameters at larger scale, such as the dimensional distribution $N(D)$, the precipitation rate $R$, the charge density $\rho$, the current density $J$, and the radar reflectivity factor $Z$. These parameters have a twofold interest: first, they possess a higher physical sense than individual measures, and second, they can be compared to radar data and model outputs. Their calculation is based on statistical considerations. One problem arises in the determination of global parameters: as the camera induces a dead time between two successive images, all the particles are not detected. A correction, implying the use of probabilities,

<figure><img src="image_12.png" /><figcaption>FIG. 11. Relationship between the actual charge (in absolute value) and the displayed charge for drops falling along the central and peripheral axes.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>FIG. 12. Error on charge measurement between centered and off-center trajectories inside the induction cylinder (right).</figcaption></figure>

must be applied to our measurements in order to take the unobserved particles into account. In the following, $V_{bal}$ is the ascent velocity of the balloon. Given the video sampling period $T = 20$ ms (50 images per second) and the time $t = h/[V(D) + V_{bal}]$ during which a particle of diameter $D$ falls with the velocity $V(D)$ in the field of view of the camera of average height $h = 4.5$ cm (see Fig. 2), the probability with which a particle can be observed is $P(D) = 1$ if $t > T$ and $P(D) = t/T$ if $t < T$. This probability requires thus the knowledge of the velocity, which is calculated for only four categories of particles for simplicity (raindrops, graupels, aggregates, and hail) according to the various methods available in the literature (Locatelli and Hobbs 1974; Beard 1976; Ferrier 1994). The velocity calculation is also needed for the determination of the sampling volume. The first parameter that can be determined is the size distribution $N(D)$ for each category of particles. It is given by

$$N(D) = \frac{n(D)}{P(D)\Delta tA[V(D) + V_{bal}]\Delta D}, \quad (3)$$

where $n(D)$ is the number of particles of diameter $D$ observed during the time $\Delta t$, $A$ is the cross-sectional area of the sensor, and $\Delta D$ the bin size. The precipitation rate $R$ is calculated for spherical precipitating particles (Sauvageot 1992) according to

$$R = \frac{\pi}{6} \frac{1}{S\Delta t} \sum_{D} \frac{n(D)}{P(D)} D^3. \quad (4)$$

Another parameter that can be determined is the charge density $\rho$. Its calculation is the same as that presented by MacGorman and Rust (1998), except that each term of the sum is divided by the detection probability which is an averaged probability:

$$\rho = \sum_{i} \frac{q_i}{P_m(V_i + V_{bal})A\Delta t}, \quad (5)$$

where $q_i$ is the individual charge of the particle $i$ of velocity $V_i$. The detection probability $P_m$ is not the same as $P(D)$ that is used for the dimensional distribution. Indeed, a size-dependent probability cannot be attributed to an electric charge since this procedure would imply a size-charge relationship. Such a relationship does not exist in thunderclouds (Bateman et al. 1994) so we chose to consider an arbitrary probability that corresponds at each time interval to the average particle size of the predominant population (raindrops, graupels, aggregates, or hail).

Finally, the radar reflectivity factor $Z$ is calculated in function of the radar wavelength, the dielectric factor, and the radar reflectivity (Sauvageot 1992). The latter is computed from the individual backscattering cross-sectional areas, which are calculated according to Rayleigh and Mie theories (see Sauvageot 1992) in function of the particle size. Thus, the radar reflectivity factor is given by

$$Z = \frac{\lambda^4 10^{12}}{\pi^5 |K|^2} \sum_{i} \frac{\sigma_i}{P_i A \Delta t (V_i + V_{bal})}, \quad (6)$$

where $\lambda$ is the radar wavelength, $|K|^2$ is the dielectric factor of hydrometeors that depends on the phase (liquid or ice) of the particles, and $\sigma_i$ is the backscattering cross-sectional area of the particle $i$.

We must be careful concerning the validity of the use of statistics. The probabilistic method would work well only if the number of particles observed in a given size bin is sufficiently high. To take into account this problem, we must adjust the duration $\Delta t$ in such a way that the number of particles in each size bin is important enough. In this way the various parameters [$N(D)$, $R$,
$\rho$, and $Z$] are averaged in the vertical. Another possibility is to diminish the number of size bins in order to increase the number of particles in each one. We have to mention that our objective is to launch sounding balloons in thunderclouds covered by simultaneous radar observations. The comparison with radar data should help to evaluate the best sampling period to improve the statistical representation of the minority populations in the determination of the various parameters.

For all calculations mentioned above, the duration $\Delta t$ has also to be adjusted in function of the parameter. When compared to disdrometer data, the parameters $N(D)$ and $R$ require a duration of 1 min corresponding to the sampling rate of the disdrometer, at the condition that the precipitation rate is high enough (see section 6). If radar data are considered, the sampling duration necessary for the calculation of $N(D)$, $R$, and $Z$ must be adjusted in such a way that the height of the videosonde sampling volume must be at least of the order of the height of the radar beam. At last, the duration used for the determination of $\rho$ must match the period during which the vertical variation rate of the electric field is of uniform polarity.

One can mention the possible complicated motion of the rigging supporting the videosonde. For example, oscillation or rotation could affect the trajectory of the particles inside the videosonde and consequently induce an underestimation of the particle concentration. To prevent an uncontrolled rotation, a triangular sail is used on one side of the rigging.

## 6. Intercomparison experience at the ground with a disdrometer

In order to estimate the videosonde performances and to validate the method used for the calculation of some microphysical parameters, we realized a calibration experiment which consists in comparing videosonde observations with measurements made by a disdrometer. The disdrometer is a measuring instrument intended to study rainfall characteristics at the ground. It converts the vertical momentum of a drop that impacts the sensor into an electrical signal the amplitude of which depends on the drop diameter. The impact area of the sensor is 50 cm², which is almost 4 times larger than the horizontal section of the videosonde sampling volume (11.8 cm²). The detectable raindrop size ranges from 0.3 to 4.9 mm every 0.2 mm with an accuracy of ±5%. The large particles for which size exceeds 4.9 mm are stored in the last bin size (4.7–4.9 mm). The dimensional distribution of raindrops and the rain rate are provided every 60 s. For this 1-min period, the sample volume of the disdrometer for sizes varying from 0.4 mm (average of the first size bin) to 4.8 mm in diameter (average of the last size bin) ranges from 0.48 to 2.72 m³. For the videosonde and for the same sizes, the sample volume ranges from 0.11 to 0.64 m³ if the probability with which the particles can be observed is not taken into

account, and from 0.11 to 0.16 m³ if the probability is considered.

Both sensors—the videosonde and the disdrometer—were simultaneously submitted to the same artificial showers produced from a tower at 15-m height, which allows all drops to reach their terminal velocity. Data acquisition was realized during three showers for a total duration of 17 min. The actual total number of raindrops observed during this time is 17 260 for the disdrometer and 1658 for the videosonde. Some images are given as example in Fig. 13. We deliberately produced high rainfall rates in order to get reliable data in as short a time as possible so that the data analysis was not too long. By doing so, we probably induced more splashing leading to a possible change in the rain distribution that should mostly affect the small sizes.

The inlet area of the sensor is an ellipse of semiaxes $a = 2.5$ cm and $b = 1.5$ cm (Fig. 3). For a spherical particle of radius $r$ falling vertically, one can define the no-contact ellipse as the ellipse of semiaxes $a-r$ and $b-r$ corresponding to the positions of the particle for which no contact with the inlet is possible. The probability of no contact between the spherical particle and the inlet of the sensor is therefore the ratio of the no-contact ellipse area over the inlet area, that is, $(a-r)(b-r)/ab$. Consequently, the probability of contact is $P_c = 1 - (a-r)(b-r)/ab$. The larger the drop, the higher the probability of contact. This one ranges between 2% for the smallest particles (diameter of 0.3 mm) and 24% for the largest ones (diameter of 5.3 mm). This calculation is only valid for raindrops falling vertically, which is roughly the case in this experiment. Taking into account particle incidence angle in the case of tilted trajectories due to possible motion of the airflow relative to the videosonde is complex because all the angles are not equally probable and depend on particle size. If particle splashing occurs, several droplets are present on the same image and this image is rejected from the data.

The overall rainfall rate deduced from the videosonde data (142.8 mm h⁻¹) is in very good agreement with that measured by the disdrometer (146.6 mm h⁻¹). Here, the videosonde underestimates the rainfall rate by only 2.6%. A more detailed comparison between both sensor performances arises from the raindrop distributions displayed in Fig. 14. At first sight, the dimensional distribution deduced from videosonde measurements fits well with the one given by the disdrometer, whether we consider the number of drops or whether we consider drops sizes. In particular, both curves perfectly match for the larger sizes even though both sensors have neither the same surface nor the same acquisition rate (only 50 pictures per second for the videosonde). It is all the more significant as large particles fall with the highest velocities, which reduce the probability to be detected by such a video sensor. The last bin of disdrometer data is an accumulation of all the out of range large drops. Thus, the fact that the disdrometer peak for diameters between 4.7 and 4.9 mm is not detected by the video-
<figure><img src="image_14.png" /><figcaption>FIG. 13. Examples of raindrop images obtained with the videosonde during the intercomparison experiment.</figcaption></figure>

sonde does not constitute a failing of this one. In the same way, the underestimation of the number of drops around 2.1 mm could probably arise from the electronics of the disdrometer. It has been shown that irregularities in transfer function of the electronics used for diameter classification lead to secondary peaks, which can be

considered as artifacts (Sheppard 1990; Sauvageot and Lacaux 1995). Therefore, the peak around 2.1 mm in the disdrometer data could appear at the expense of surrounding diameter classes.

However, one can notice in Fig. 14 that the present microphysics sensor underestimates the number of very small particles (diameter from 0.2 to 1 mm) and over-estimates raindrops between about 1 and 2 mm in di-ameter. This discrepancy could originate 1) from the rejection of several small drops from the videosonde data because their shadows were impossible to detect, 2) from the relatively large errors that exceed 10% for this range of size (see Fig. 7) and that could lead to forgetting small drops in the videosonde data analysis at the benefit of larger ones, and 3) from windy con-ditions that had tilted the small drop trajectories in such a way that the camera located in the lower part of the videosonde could not have detected them whereas the impact area of the disdrometer located at the top of this sensor can detect them. Nevertheless, more and more data could probably lessen these differences since this behavior is not systematically observed in each minute of each shower. As a matter of fact, the opposite be-

<figure><img src="image_15.png" /><figcaption>FIG. 14. Dimensional distributions deduced from the videosonde and the disdrometer measurements. The Poisson uncertainty is 0.547 for the disdrometer data and 0.402 for the videosonde data.</figcaption></figure>
havior has even been observed several times especially when larger drops splashed in the videosonde. In these cases, several drops of small size where present on the same picture, leading to an overestimation of the small drop number and an underestimation of the large drop number. In such a case, the size distributions from both sensors appear similar, but that from videosonde is shifted toward smaller sizes.

## 7. Conclusions

The characteristics and performances of a new microphysics sensor dedicated to thunderstorm soundings have been presented. The videosonde is based on simple technology in order to reduce the cost of the sensor, which is lost after launch. The use of a CCD camera allows us to determine the phase, the shape, and the size of precipitating hydrometeors, which can range in size from 0.5 mm to 2 cm with an estimated average accuracy varying from about 13% to less than 2.5%, respectively. The videosonde is designed to detect the particles singly. Therefore, the display of the electric charge measured with an induction cylinder is synchronized to the passage of the particle in the field of view of the camera. The electronic system is capable of measuring charges ranging from ±1 to ±400 pC with an average accuracy of about 3.2% whatever the trajectory inside the sensor is. However, errors as high as 18% have been detected for charges between -50 and +50 pC, but it is questionable whether the system of drop production is responsible for this discrepancy.

The determination from the videosonde data of various parameters like the precipitation rate *R*, the dimensional distribution *N(D)*, the electric charge density *ρ*, and the radar reflectivity factor *Z* has also been presented. These parameters are interesting since they allow a direct comparison with simultaneous radar measurements or electric field soundings. For example, the last comparison can provide the part played by precipitating particles in the cloud charge density inside a thunderstorm. The calculation of *R* and *N(D)* has been tested during a ground experiment involving the videosonde and a disdrometer that were simultaneously submitted to artificial showers. The precipitation rates perfectly match and the size distributions are in very good agreement except for the smaller sizes that were probably much more sensitive to windy conditions.

This new videosonde has not flown in a thunderstorm cloud yet. It is scheduled to be launched in the summer of 2004 from the Centre de Recherches Atmosphériques de Lannemezan, France, close to the Pyrénées.

**Acknowledgments.** This work was supported by the Programme Atmosphère et Océan à Multi-échelles of the INSU and by the ASUPS BQR of the Observatoire Midi-Pyrénées and the Université Paul Sabatier. The authors thank for their help Yves Meyerfeld and all the

members of the Centre de Recherches Atmosphériques de Campistrous.

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