Study of a precipitating cloud system using Chung-Li VHF radar



[1] In this paper, Chung-Li VHF radar returns from hydrometeors and reflectivity fluctuations associated with “showery” precipitating cloud systems are studied in detail. The space antenna drift method is applied to three rain gauges in the vicinity of the radar to obtain the drift velocity of the rain cells during the passage of the precipitation cloud systems. The drift velocity of the rain cells is found to be 13.4 m/s and moving in the southwest to northeast direction, and these results are in good agreement with the simultaneous observations of the horizontal wind velocity observed with the radar. VHF radar can see the frontal structure so clearly because of its sensitivity to thermal stratification in the atmosphere. A composite analysis of the turbulence and precipitation echo intensity and vertical air velocity indicates that the vertical air velocity plays a vital role for the occurrence of showery precipitation. The mean raindrop diameters are estimated from the air motion adjusted Doppler velocities resolved by the radar. Observational results show that the mean raindrop sizes of 0.5–3.5 mm are primarily responsible for the showery precipitation. The ambient air motions and turbulent broadening effects are discussed before and during the passage of the precipitation event. The analysis suggests that the beam-broadening effect needs to be considered if the information of the drop size distribution is to be estimated from the observed Doppler spectral width.

1. Introduction

[2] Wind profiler has become a powerful tool in the remote sensing of clear-air turbulence and precipitation in the atmosphere. For several years, meteorological Doppler radars at microwave frequencies have been the standard tools for understanding dynamic properties of precipitating cloud systems in the troposphere [Doviak and Zrnic, 1984]. However, these radars do not directly observe ambient air motion, rather, the velocity of precipitation particles within a particular region. On the other hand, use of the wind profiler technique for more accurate measurement of turbulence and rainfall properties has proliferated. This is because the ability of VHF/UHF atmospheric wind profilers to provide frequent vertical profiles of horizontal air motion under almost any weather condition has made them a unique and valuable tool, now used in many observational research programs and in operational applications. It has been established that the Doppler power spectra measured by VHF clear-air radars are used to determine both vertical air motion and hydrometeor terminal velocity simultaneously, in the conditions of moderate to heavy precipitation [Green et al., 1978; Fukao et al., 1985a; Wakasugi et al., 1986; Sato et al., 1990; Chu et al., 1991; Rajopadhyaya et al., 1993; Cifelli and Rutledge, 1994; May and Rajopadhyaya, 1996; Rao et al., 1999; Krishna Reddy et al., 2000]. This result was extended to higher-frequency (UHF) clear-air radars, such as 404 MHz [Larsen and Rottger, 1987; Wuertz et al., 1988], 915 MHz [Ecklund et al., 1988, 1995; Gossard et al., 1990; Rogers et al., 1993; Gage et al., 1994, 1996; Carter et al., 1995; Williams et al., 1995, 2000; Ralph et al., 1995; Tokay et al., 1999; Atlas et al., 1999] and 1.3 GHz [Renggono et al., 2001; Krishna Reddy et al., 2001], although for UHF profilers the vertical air motions are often masked by Rayleigh scattering from precipitation. This method was later extended for simultaneous measurements from both VHF and UHF radars [Currier et al., 1992; Chilson et al., 1993; Maguire and Avery, 1994; Rajopadhyaya et al., 1999; Cifelli et al., 2000].

[3] A number of different techniques have been utilized to separate the clear-air vertical motion from the hydrometeor fall speeds in VHF wind profiler spectral data. Some of the procedures include first moment analysis of the Doppler spectra [Clark and Carter, 1980], least squares fit of the Doppler spectra with one or two Gaussian approximations [Chu et al., 1991; Yoe et al., 1992; Rao et al., 1999], and least squares fit of the Doppler spectra with a Gaussian distribution for the turbulent echo and a drop size distribution approximation for the hydrometeor echo of the spectra [Wakasugi et al., 1986; Sato et al., 1990; Currier et al., 1992]. Rajopadhyaya et al. [1993] separated the convoluted spectra (of precipitation echo and turbulence echo) without assuming any specific shape to estimate the drop size distribution. Cifelli and Rutledge [1994] developed a combination of Sato et al. [1990] and Rajopadhyaya et al. [1993] methods for understanding the vertical motion structure over Darwin, Australia, maritime continental mesoscale convective systems. Cohn et al. [1995] employed two methods to separate the turbulence and precipitation echo from the Doppler spectrum. The first method assumes that the minimum point between the two peaks represents a boundary between the spectra. The disadvantages of this method are uncertainties in the estimations of power and spectral width, because of the overlapping of two echoes. In the second method Gaussian distribution was assumed for both refractivity and precipitation echoes in the Doppler spectrum.

[4] Moreover, unlike the conventional meteorological Doppler radar, the vertically pointing VHF radar can directly determine the fall velocity spectrum of hydrometeors, size distribution of liquid and solid precipitation particles, vertical air motion associated with intense precipitation, interaction between hydrometeor and the ambient atmosphere, vertical height distribution of precipitation and the drift velocity of a precipitating cloud system, if the velocity can be related to the size of the falling droplets. In addition to these parameters, bright band in the reflectivity profile of hydrometeors can also be observed by VHF radar [Fukao et al., 1985b]. Chu et al. [1991] have studied the precipitating cloud system during the passage of typhoon Susan through the Taiwan area. They were not focused much on the behavior of refractivity fluctuations and precipitation.

[5] In this paper, we discuss the effect of precipitation on stable stratified layers associated with a tropical precipitating cloud system observed on 25 December 1994. The characteristics of Chung-Li VHF radar are described briefly in section 2. In section 3, the observational results of Doppler spectra of precipitating atmosphere are discussed in detail. Three rain gauges located close to the VHF radar are utilized to measure the drift velocity of the rain cells during the passage of frontal structure over Taiwan area. The connections between several atmospheric and precipitation parameters deduced from the observed VHF Doppler spectra, e.g., turbulence and precipitation echo powers, vertical velocity, terminal velocity of the hydrometeor and raindrop size distribution (DSD), will be discussed. The beam-broadening effect due to the turbulence effect on the observed Doppler spectral width of turbulence and precipitation will also be discussed. Some conclusions will be given in section 4.

2. Experiment Description

[6] The VHF radar is located at the campus of National Central University of Chung-Li (24.58°N; 121.0°E) in Taiwan. The whole antenna array of this radar consists of three identical and independent modules. Each antenna module is a square array with 64-Yagi antenna elements (8 × 8). The whole antenna array is arranged as an equilateral triangle with length 45, 45 and 40 m, respectively. The operational frequency of this radar is 52 MHz (corresponding to 5.77-m wavelength) and the peak-transmission power is 180 kW. The maximum duty cycle 2%, and the pulse width can be set from 1 to 999 μs. The antenna beam width for each module is 7.4° and 5° for whole array. The radar beams can be pointed not only toward zenith, but also toward northwest, northeast, southeast, and southwest with fixed zenith angle 17°. The MST subarray is employed for wind measurements in the mesosphere, stratosphere, and troposphere. For more information on the characteristics of the Chung-Li VHF radar and the MST subarray, see Rottger et al. [1990].

3. Observations, Results, and Discussions

[7] The weather map remains one of the key tools for the study of atmospheric processes and the prediction of the weather. A synoptic weather map observed on 25 December 1994 is shown in Figure 1. From the figure we can see two tropical depressions: One is centered northeast of Taiwan, and other one is centered west of the Pacific Ocean. From the figure it is also observed that a high-pressure system is moving from mainland China toward southeast. As we know that the tropical depression is a humidity and warm front whereas the high-pressure system is a cold and dry. One can anticipate unstable weather conditions when tropical depression approaches Taiwan. The important distinguishing feature between the less active tropical depression and the cyclone is that tropical cyclones develop a “warm core,” whereas the former (smaller features) possesses a “cold core,” the result is shower activity.

Figure 1.

Synoptic weather map observed on 25 December 1994.

[8] Figure 2 shows time series of rainfall rate observed on 25 December 1994 at three locations, Chung-Li (C1C52), Hsin-Wu (C0C45) and Yang-Mei (C1C50) about 1 km east, 8 km south and 7.8 km west of the Chung-Li VHF radar site, respectively. The rain gauge data is collected with 1-min resolution. According to C1C52, C0C45 and C1C50 rain gauges the total rainfall of 15, 14 and 14-mm is recorded, respectively, on 25 December 1994. The time series of the rain rate shows less similar variations for the three stations. From the rain intensities observations we can anticipate a widespread showers over this region. As we know that vertical air motions largely control the aerial extent, intensity, and lifetime of a precipitation cloud system.

Figure 2.

Time series of rainfall rate observed at Chung-Li, Hsin-Wu and Yang-Mei using conventional rain gauges on 25 December 1994.

3.1. Estimation of Advection Speed and Direction of the Precipitating Cloud System Observed on 25 December 1994 From Surface Rain Gauges

[9] In general, the subtropical region's heavy rain was found in widespread as well as showery precipitation situations occur in compact groups rather than to be randomly scattered. The groups are often in the form of rainbands associated with frontal surfaces. Topographic effects, as well as fronts, can affect the structure and development of rain cells. Although the effects have been documented to some extent but it is not yet fully understood the patterns of the observed precipitation. The rainfall rate is a function of position on the surface and time. Following the practice in the theory of random processes, it is possible to define the autocorrelation function of rainfall rate in time or space. Earlier, Zawadzki [1973] utilized space autocorrelation function to describe the fine-scale structure of widespread rain. To understand the structure of the showery precipitation, we have utilized the radar and three rain gauges. Chu et al. [1995] and other groups applied the spaced antenna drift (SAD) method to ionospheric experiments for measurement of horizontal drifting velocity of electron density irregularities. We adapted similar procedure to measure the horizontal drift velocity of the rain cells during showery precipitation.

[10] We applied SAD method to the rainfall data collected from the three stations to measure the horizontal drift velocity of the rain cells. Figure 3 shows the schematic diagram of a rain pattern moving through rain gauges and Chung-Li VHF radar. The closed curves represent the contours of the rain cells, V is the drift velocity of the pattern, a, b and c are the distance between rain gauge pairs 2-3, 3-1 and 1-2, respectively. The dashed line DD′ is a reference line and is parallel to the drift direction of the rain cells on the ground and connects the rain gauges at the apex point 2. The dotted lines are drawn such that they are normal to the dashed line and line with the rain gauge array at the corresponding apex points. With the help of these complementary lines, the distances L and M can be estimated from the geometry of the figure in order to deduce the drift velocity of the rain cells. On the basis of this schematic plot, it is clear that the following equations can be obtained readily:

equation image
equation image
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where t1 and t2 are time delays between the time series pairs of the rain intensities recorded by the rain gauge pairs 1-2 and 1-3, respectively. From equations (1), (2) and (3), we have

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By substituting equation (4) into equation (1) and rearranging the formula, the angle α can obtained by the following equation:

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As a result, the drift velocity, V of the rain pattern can be estimated by using the following equation:

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Equations (5) and (6) indicate that the estimation of t1 and t2 from the observed rainfall rate plays a vital role in determining α and V. The t1 and t2 are estimated through the calculation of the cross-correlation function of the corresponding time series of the rainfall rate.

Figure 3.

Schematic plot showing the geometry of the three rain gauges with a moving rain cell, where the curves represent the contours of the pattern. V is the drift velocity of the pattern, and the reference line DD′ is chosen arbitrarily.

[11] Estimation of time delays between the time series of the rain intensity recorded by three tipping bucket rain gauges plays a key role in deriving drift velocity of the rain cells. The correlation analysis is performed for the rain intensities recorded by each pair rain gauges. The cross-correlation function, ρ(τ), of the time series x(t) and y(t) is defined as follows:

equation image

where τ is the time lag. Apparently, the maximum cross-correlation coefficient, ρm (τ), occurs at a specific time lag, τm, which represents the time shift between the time series x(t) and y(t). The sign of τm depends on whether the time series x(t) leads or lags behind the time series y(t). By definition, if τm is positive, this indicates that y(t) leads x(t), and vice versa. Figure 4a shows the cross-correlation functions of the rain intensities shown in Figure 2. As indicated in Figure 4a, the time delays between ρ12, ρ23 and ρ31 pair of rain intensities obtained from rain gauge 1-2, 2-3 and 3-1 are −7.74, 5.77 and −0.29 min, respectively. By substituting these time delays into equations (5) and (6), the horizontal drift velocity of the rain cells can thus be obtained, as presented in Figure 4b. It shows that observed drift velocity of the rain cell is 13.4 m/s from the direction of 44.6° in southwest to northeast. These results are fairly good agreement with the horizontal wind velocity observed with the VHF radar. Horizontal wind velocity observed over Pan-Chiao rawinsonde station around 0800 local time (LT) also confirms the orientation of the wind direction and the magnitude of the wind velocity (figures not shown here).

Figure 4.

(a) The cross-correlation functions of the pairs of rain rate as shown in Figure 2. (b) The observed drift velocity and direction of the rain cells on 25 December 1994.

3.2. Chung-Li VHF Radar Observation of Precipitating Cloud System

[12] Chung-Li VHF radar has been used in the recent past to study the adverse weather/meteorological phenomena, such as typhoon passage, Mei-Yu cold front due to the fact that precipitation measurements can be made at the same time as wind observations [Chu et al., 1999]. Due to these capabilities of Chung-Li VHF radar, we utilized this radar to investigate the useful information on the precipitating cloud systems and their vertical structure during (tropical depression) warm front over subtropical region of Taiwan.

[13] The echoes from refractive index fluctuations and precipitation particles can be identified when the precipitation is within the radar volume. Figure 5 shows an example of the Doppler spectra observed by vertical beam from 1.8 to 7.8 km with 300 m of height resolution on 25 December 1994 around 2218:44 LT. The first peak located near the position of the zero Doppler frequency is attributed to the Bragg scattering from refractive index fluctuations, and the second peak is due to Rayleigh scattering from the precipitation particles. These Doppler spectra, and all those presented in detail in this paper, were taken at vertical incidence. It is obvious from Figure 5 that the stronger echoes intensities and narrow Doppler velocities (centered near 0 ms−1) correspond to the radar returns of refractivity fluctuations. The fall velocity of the particles up to −7 ms−1 is caused due to the hydrometeors. We can separate the two echoes and find the reflectivity, mean velocity, and spectral width associated with each echo.

equation image

where S(ω) is the observed precipitation Doppler spectrum, St(ω) represents the Doppler spectrum of refractivity fluctuations, and Sp(ω − ωo) is the size distribution of precipitation particles in the Doppler spectral domain at Doppler frequency ωo, and ∗ represents the convolution operator. Note that the shape of St(ω) is usually assumed to be Gaussian because of the beam-broadening and turbulent broadening effects [Woodman and Chu, 1989]. Superficially, the shape of Sp(ω − ωo) is not Gaussian due to the exponential form or Gamma pattern of the drop-size distribution [Marshall and Palmer, 1948; Ulbrich, 1983]. However, because the radar echo power from precipitation is proportional to the 6th power of the diameter of the precipitation particle, the pattern of Sp(ω − ωo) will be quasi-Gaussian in the Doppler spectral domain [Atlas et al., 1973]. Consequently, for the sake of mathematical simplicity, the shape of Sp(ω − ωo) can be treated with Gaussian form, causing the reasonable approximation of Gaussian pattern S(ω). The spectral components for turbulent refractivity and precipitation can be separated unambiguously in the Doppler spectral domain as long as the radar beam is steered in the right direction and the radar parameters are set appropriately. The echo power, mean Doppler frequency shift, and spectral width for these two components are estimated separately with the least squares method, in which the Gaussian curve, is employed to best fit the corresponding Doppler spectral component [Chu et al., 1991, 1999; Cohn et al., 1995; Rao et al., 1999]. The data also contain spectra with only a single peak. In that case we must decide if the echo is from clear air or hydrometeors. This is done based on the mean fall speed and power of the echo.

Figure 5.

Height-intensity contour of the observed Doppler spectrum on 25 December 1994 around 2223 LT.

[14] Figures 6a and 6b present time-height distributions of the VHF echo power from the refractivity fluctuations and precipitation observed by using the vertical pointed radar beam. Roughly speaking, the variation in echo power is the indicative of change in atmospheric stability. Stratified stable layers up to 3 km height are observed from 0007 to 1015 LT and a second, more-or-less stable layer, at ∼4 km from 0007 to 0200 LT. Because of the different radiation balances between night and daytime conditions stable lower atmospheres tend to dominate at night, and unstable ones at day. Figure 6b shows several intermittent precipitation events (circled characters indicated in Figure 6b) are caused due to the passage of precipitating cloud systems. Around 0110 and 0255 LT, precipitation is detected by the VHF radar, but no precipitation is observed at the surface (as shown in Figure 2). The lifetime of the individual precipitation showers ranges from 20 to 168 min. In this study, we mainly focused on predominant three precipitation echoes (F, G and H) observed around 1705, 1807, and 2055 LT. In the case “F” enhanced precipitation echo power, which is almost continuously observed in Figure 6b at an altitude of 4.2 km, is considered to be a bright band where falling particles pass through the melting (0°C) layer. According to Szoke and Zipser [1986], one of the necessary conditions for the formation of bright band is the relatively small vertical air velocity (less than 2 ms−1). Examining the distribution of vertical velocity (as shown later in Figure 7a) reveals that for the present precipitation event the vertical air speed is weak that leads to the formation of the bright band. It is noteworthy to observe that inside the bright band the precipitation echoes are enhanced due to change in precipitation state from ice to liquid water and by the aggregation of ice crystals [Fletcher, 1972; Battan, 1973; Fukao et al., 1985b]. In Figure 6a it is also noticed (F, G and H) that the turbulence echo intensity variations during the occurrence of precipitation. The radar refractivity is a function of atmospheric humidity, temperature and pressure. The interaction between precipitation particles and the ambient atmosphere leads to the following effects. One is the cooling effect, arising from the absorption of latent heat associated with the evaporation of the raindrops and melting of the ice particles. This effect will lower the atmospheric temperature and changes the refractive index. The other one is the vaporizing effect, due to the release of water vapor through the processes of ice sublimation and raindrop evaporation. This effect will increase the atmospheric water vapor content and also influences the atmospheric refractive index. These two effects occur simultaneously in the precipitating atmosphere. However, it is believed that the latter one is more important than that of the former one. This because the fractional change of humidity, due to the turbulent-mixing process, is greatly larger than that of temperature below the middle troposphere (for example, below 8 km) over the Taiwan area [Chu et al., 1990]. Moreover, from Figures 6a and 6b it is evident that precipitation plays a vital role in atmospheric turbulence and turbulence mixing.

Figure 6.

Time and height cross section of (a) turbulence echo power and (b) precipitation echo power observed on 25 December 1994.

Figure 7.

Time and height cross section of (a) vertical velocity and (b) the hydrometeor terminal velocity deduced from the vertical beam on 25 December 1994.

[15] Radar measurements of vertical velocities are important in improving our understanding of the turbulence and precipitation growth in the showery precipitation. Figure 7a shows the height-time distribution of the vertical air velocity observed on 25 December 1994. The most striking feature of the vertical wind structures is random variations in the updrafts/downdraft before and during the showery precipitation. We believe that (in this study) the weak updraft/downdraft plays a major role in the formation and disappearance of turbulence layers and also in precipitation echoes during the rain “showers.”

[16] Figure 7b is the height-time variation of the terminal velocity (i.e., updrafts and downdrafts are removed from the fall velocity of the hydrometeor) deduced from the precipitation Doppler spectra observed by the vertically pointed radar beam. During the showery precipitation, above 4.2-km the frozen hydrometeors (snow/snowflakes) show terminal velocity of 1–2 ms−1. At the melting level, near 4.2, the melting droplets accelerate and a relative maximum (bright band) reflectivity can be observed (Figure 6b). In the rain region, i.e., from 4 to 1.8 km, the terminal velocities up to 8 ms−1 are observed. Our results are consistent with the earlier investigations with VHF radar [Fukao et al., 1985a, 1985b; Wakasugi et al., 1985; Chu et al., 1991] and also with conventional meteorological Doppler radar [Atlas, 1964; Battan, 1973].

[17] One of the most critical parameters deduced from radar measurements, and one of central importance in precipitation microphysics, is the distribution of the water particles as a function of diameter. To find the raindrop diameter accurately from Doppler radar spectra it is necessary to have accurate measurements of the particle terminal speeds, which in turn requires accurate estimation, made by the motion of the air through which the drops are falling. It is well known that the mathematical relation between raindrop terminal velocity v and its diameter D can be formulated empirically as follows [Gunn and Kinzer, 1949]:

equation image

where v is in meters per second and negative downward, D is in millimeters, ρo is the air density at the ground level, and ρ is the air density at the height of observation. This equation is, however, valid only if D is larger than Dmin that is the diameter to make the velocity, v is zero.

equation image

[18] We assume that there is no raindrop that has the drop-size smaller than Dmin for estimation of raindrop size distribution (DSD) during the passage of showery precipitating cloud systems on 25 December 1994. Figure 8 shows the estimated DSD using equation (9). The figure shows that the radar returns scattered from the raindrops with diameters from 1.75 to 3 mm contribute to the major portion of the observed precipitation echo power.

Figure 8.

Histogram showing the raindrop size distribution.

[19] The Doppler spectral width is an extremely important VHF radar echo parameter. Most of the atmospheric information can be evaluated from this radar parameter. However, there are quite a few physical mechanisms that can contaminate the width of the Doppler spectrum. For example, the beam-broadening effect, wind shear effect, drop-size-distribution-broadening effect, and gravity wave oscillation effect will broaden the Doppler spectral width. This problem is present for all VHF Doppler radars [Nastrom and Eaton, 1997] and especially more severe for the off-vertical beams [Nastrom and Tsuda, 2001]. The Doppler spectral width will also be narrowed by the aspect sensitivity for vertical or close to vertical pointing radar beam. Because the broadening and the narrowing effects are coexisting in the observed Doppler spectrum, the estimation of true atmospheric information from spectral width will be impossible if the contaminating factors are not thoroughly removed from the spectrum [Woodman and Chu, 1989]. Beam broadening is caused by the horizontal wind drifting of the atmospheric refractive index fluctuations across the radar beam. For the conventional microwave meteorological radar the beam width is always narrow (<1°); hence the beam-broadening effect can be ignored. Whereas, the VHF radar has the broad antenna beam width, and the beam-broadening effect cannot be neglected. Figure 9 shows the spectral width of the turbulence echo, which is separated from precipitation echo on 25 December 1994 from 0007 to 0000 LT. From this figure we anticipate that beam broadening of turbulence Doppler spectrum is significant and should be taken into consideration in the analysis of Doppler spectral width for further applications.

Figure 9.

Spectral width of the turbulent echo deduced from vertical beam.

[20] As discussed above, turbulence medium has influence on the Doppler velocities associated with the hydrometeors. The precipitation particles are carried along with the turbulence, which have the effect of smearing their backscattered signal across the frequency bins in the Doppler spectrum. This in turn broadens the spectrum, which leads to an erroneous assessment of the actual diameters of the particles and their distribution function. It is possible to remove the contribution of the clear-air spectral width caused by beam-broadening and turbulent broadening effects from the spectral width of precipitation to estimate the hydrometeor size distribution provided the precipitation particles are frozen in the background wind [Wakasugi et al., 1986; Gossard et al., 1990]. Under this assumption, the mathematical relation of the Doppler spectra between precipitation particles and refractivity fluctuations for vertically pointed radar can be formulated as in equation (8). The information of precipitation particles can be separated from Sr(ω), which is contaminated by St(ω), through the following relation:

equation image

where σp2, σ2, and σt2 are the variances of Sp(ω − ωo), S(ω) and St(ω), respectively. Because σp can be treated as an indicator of the breadth of hydrometeor size distribution, the larger σp is, the broader the size distribution will be.

[21] The main factors influencing the magnitude of spectral width from precipitation are the reflectivity per unit area of the particles, their backscattering cross-sectional area and the distribution of the drop sizes. The reflectivity of each particle will depend on the complex index of refraction of its constituents. In conditions of vertical mixing in precipitation, the different hydrometeors become separated into layers according to the thermal structure of the atmosphere. The various dependencies of radar return then become important [Smith, 1986]. Figure 10 shows the distribution of precipitation particle spectral width observed on 25 December 1994. Above the melting layer, where the temperature is well below 0°C, are small ice crystals of low reflectivity that give a narrow spectral width. As indicated in the figure, in the height ranges at the bright band a broader Doppler spectral width is observed due to rapid increase in the dielectric constant of hydrometeors at the top of the melting layer followed by an increase in the terminal velocity of melting snowflakes toward the end of the melting process. Below the melting layer large drops tend to increase with rainfall rate and contribute to the radar reflectivity. Comparison of Figure 10 with Figures 6b and 7b shows that the relatively larger σp around bright band region coincides very well with the intense radar reflectivity, consistent with the theoretical anticipation.

Figure 10.

Spectral width of the precipitation echo deduced from vertical beam.

4. Conclusions

[22] In this paper, refractivity fluctuations and precipitation observations, made with Chung-Li VHF radar during “showery” precipitating cloud systems passing over Taiwan Island on 25 December 1994 due to tropical depression are studied in detail. Three rain gauges are employed to measure the drift velocity of the rain cells in showery precipitation. The drift velocity of the rain cells is found to be 13.4 m/s and moving in the southwest to northeast direction and these results are in good agreement with the simultaneous observations of the horizontal wind velocity observed with the VHF radar. The results show that the radar is capable of detecting the showers by measuring the vertical speed of precipitation and by resolving the vertical fine structure. VHF radar can see the frontal structure so clearly because of its sensitivity to thermal stratification in the atmosphere. A composite analysis of the turbulence and precipitation echo intensity and vertical air velocity indicates that the vertical air velocity plays a vital role for the occurrence of showery precipitation. The observed vertical velocities are small and their influence is negligible to the terminal velocity of precipitation particles. From the showery precipitation the bright band structure is found around 4.2 km. The terminal velocity of the snow/snowflakes and raindrops are about 2 and 8 ms−1, respectively. The mean raindrop diameters are estimated from the air motion adjusted Doppler velocities resolved by the VHF radar. Observational results show that the mean raindrop sizes of 0.5–3.5 mm are primarily responsible for the showery precipitation. Our observational results suggest that the beam-broadening effect needs to be considered if the information of the drop size distribution is to be estimated from the observed Doppler spectral width.


[23] K. Krishna Reddy is thankful to the authorities of Frontier Observational Research System for Global Change (FORSGC), Yokohama, Japan, for the facilities provided to carry out this work.