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Article

Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front

by
Shichao Zhu
1,*,
Ye Yuan
1,
Yue Wu
2 and
Ying Zhang
3
1
Anhui Weather Modification Office, Hefei 230031, China
2
Hefei Meteorological Bureau, Hefei 230041, China
3
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(7), 1088; https://doi.org/10.3390/atmos13071088
Submission received: 28 April 2022 / Revised: 6 July 2022 / Accepted: 7 July 2022 / Published: 10 July 2022
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Abstract

:
Cloud Vertical Structure (CVS) plays a crucial role in determining atmospheric circulation and the hydrological cycle. We analyzed the CVS in Stratiform Clouds with Embedded Convection (SCEC) occurring in the mei-yu front over central-eastern China based on the conjunction of the S-band Doppler weather radar, the C-band Frequency Modulation Continuous Wave (C-FMCW) rad ar, and the Microrain Radar (MRR). Our results showed that both the melting layers and the rain rate were unevenly distributed in the three SCEC cases, and there was a thicker melting layer and a larger rain rate in the embedded convection. In the stratiform regions, the vertical velocity of particles in the upper region of the melting layer was generally in the range of 0–4 m·s−1, and increased rapidly to 4–12 m·s−1 near the bottom of the melting layer. In the case of June 28, due to the vigorous development of embedded convection, the cloud particles in the upper layer showed upward movement, and the growth rate of the particles in this region was faster than that in the surrounding stratiform regions. The vertical distributions of Drop Spectrum Distributions (DSDs) showed that the average concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km, and the collision–coalescence process of drops in the embedded convection was stronger.

1. Introduction

Clouds influence atmospheric circulation, the Earth’s hydrological cycle, and the energy budget [1,2,3,4]. The Cloud Vertical Structure (CVS) over any place significantly impacts the boundary layer dynamics and horizontal/vertical temperature gradient at the local scale, whereas on the synoptic scale, it plays a crucial role in determining the atmospheric circulation and hydrological cycle [5,6]. The CVS reflects the internal thermal and dynamic processes of the cloud body, which affects the atmospheric circulation through radiation and latent heat heating, but it is difficult to determine its degree of influence [7]. At the same time, the vertical distribution of cloud directly affects the microphysical processes in the cloud, thus affecting the occurrence and intensity of precipitation [8,9]. The accurate description of cloud structure is still difficult using numerical models [10]. Therefore, research on the vertical structure characteristics of cloud has important scientific significance and practical application value.
Radar has always been the main tool for studying the CVS [11,12,13,14,15]. In the observation of the Atmospheric Radiation Measurement (ARM) and the North American Monsoon Experiment (NAME), multi wavelength vertical detection radars are used to perform joint observations in order to determine the physical processes and vertical evolution laws in the atmosphere and precipitation clouds, with experiments showing that vertical pointing radar is an effective detection system for obtaining the vertical distribution of atmosphere and cloud [16,17]. In this study, we used two kinds of vertical pointing radars and a volume scanning radar to analyze the CVS of the Stratiform Clouds with Embedded Convection (SCEC) occurring in the mei-yu front over central-eastern China.
The mei-yu front over central-eastern China, typically occurring during the period from mid-June to mid-July, is one of three heavy rainfall periods over China [18]. Numerous observational and modeling studies have been conducted in the last three decades to examine the large-scale circulation, synoptic scale weather systems, formation mechanism, mesoscale convective systems, and cloud-precipitation morphology associated with the mei-yu rainfall [19,20,21,22,23]. Mei-yu has always been a focus of and challenge faced in meteorological research. It is the product of the joint action of multi-scale weather systems, its precipitation structure is complex [24], and there are convective organization forms on multiple scales [25]. The SCEC is an important cloud form in the mei-yu front, and it typically has a long lifetime, which may result in either continuous or intermittent precipitation over a large region [26,27,28,29,30]. Because of the presence of high ice crystal concentrations and supercooled water content in embedded convections [28,29,31], these systems can improve the precipitation efficiency of stratiform clouds by up to 20–35% [32,33,34]. Due to the limitations of detection technology, previous research on the CVS of the SCEC occurring in mei-yu fronts has remained at the synoptic scale and the mesoscale [18,25,35]. There are few studies on the CVS of the SCEC based on high temporal- and vertical-resolution radar. Therefore, the detailed vertical structure and vertical microphysical characteristics of the SCEC occurring in the mei-yu front are still unclear.
In this study, we determined the CVS of the three SCEC cases occurring in the mei-yu fronts over central-eastern China on the basis of the conjunction of the S-band Doppler weather radar, the C-band Frequency Modulation Continuous Wave (C-FMCW) radar, and the Microrain Radar (MRR). Based on the high temporal and vertical resolution of vertical pointing radar, the vertical structure of the clouds, the vertical velocity distribution of the particles, and the vertical distribution of Drop Spectrum Distributions (DSDs) were analyzed. These have important significance for understanding the precipitation mechanism of the SCEC occurring in the mei-yu front.

2. Data and Methods

2.1. Radars Introduction

The radars used in this study mainly include a S-band Doppler weather radar, a C-FMCW radar, and an MRR (METEK GmbH). Figure 1a shows the topography height of eastern China and the study area, while Figure 1b shows the locations of the three radars and Fuyang station in the study area. Both the C-FMCW radar and the MRR were placed at Shouxian station, which is a meteorological station in Anhui province. The S-band Doppler weather radar was placed in Hefei in Anhui province. Table 1 shows the main parameters of the S-band Doppler weather radar, the C-FMCW radar and the MRR. The Doppler radar data are used to analyze the macro structure of cloud, the C-FMCW radar data are used to analyze the vertical structure and the vertical velocity distribution of particles of cloud, and the MRR data are mainly used to analyze the vertical distributions of DSDs.
Quality control of the S-band Doppler weather radar data is performed using the Cinradmosaic software, which was developed by the Chinese Academy of Meteorological Sciences. The step-by-step super-refraction ground object echo recognition method based on fuzzy logic is adopted to recognize and eliminate the ground object, and eliminate the isolated echo and electromagnetic interference at the same time, so as to ensure the quality of the radar data. Cinradmosaic first controls the quality of the radar volume scan data, and then converts radar data from volume scan coordinates to three-dimensional rectangular coordinates. Based on three-dimensional basic data, it generates secondary products such as composite reflectivity.
The MRR and the C-FMCW radar are both Frequency-Modulated Continuous-Wave (FMCW) Doppler radars that adopt continuous wave systems to realize range measurement by modulating the frequency of transmitted signal and demodulating the return signal. The principle of FMCW Doppler radar for volume filling targets was described by Strauch [36].
The MRR is a vertical-pointing radar with a wavelength of 1.25 cm. By obtaining the Doppler power spectrum, the number concentration corresponding to particles with different heights and diameters is retrieved by using the empirical formula [37] of precipitation particle vertical velocity and particle size. Based on the retrieved raindrop spectrum, the changes of different raindrop spectrum parameters with time and height can be calculated to analyze the vertical distribution and evolution of precipitation with time. The advantage of the MRR is the small amount of transmitter power required for a given radar sensitivity. Usually, FM-CW radars need separate transmitting and receiving antennas. Thanks to the small transmission power (50 mW) of the MRR, a common antenna can be used here, so that no beam overlap problems need to be considered [38]. However, MRR also has some disadvantages with respect to the measurement of ice particles [38,39]. To avoid these problems, we chose a detection height range of 0–3.1 km, where the precipitation particles do not contain ice particles, so the drop spectrum detected by the MRR will be more accurate. In addition, we eliminated some profiles containing missing data.
The MRR utilized here observes rainfall parameters from the ground up to a height of 3.1 km for 31 levels at a resolution of 100 m and provides 1 min averaged raindrop number density measurements consisting of 64 bins from 0.246 mm to 5.03 mm in diameter, corresponding to a velocity range of 0.78 m·s−1 to 9.34 m·s−1. From the raw spectral power received by the MRR, the vertical profiles of the droplet number concentration N(D) can be retrieved.
N(D) can be derived from the MRR spectral reflectivity density η(D) and single particle back scattering cross sections σ(D) [38]:
N ( D ) = η ( D ) σ ( D )
where η(D) is related to the measured spectral reflectivity η(f):
η ( D ) = η ( f ) ∂ ( f ) ∂ ( v ) ∂ ( v ) ∂ ( D )
∂ f / ∂ v is Doppler relation, ∂ v / ∂ D can be calculated by empirical relation [37]:
v ( D ) = 9.65 − 10.3 exp ( − 0.6 D )
Due to the limitations of the observation height and the MRR algorithm, it is necessary to use the C-FMCW radar to jointly observe the development and evolution of clouds. The C-FMCW radar is a vertical-pointing radar with a wavelength of 5.4 cm, and it can provide the vertical structure of precipitation cloud. The C-FMCW radar was jointly developed with a newly signal processing technology by the Chinese Academy of Meteorological Sciences and the Sun Create Electronics company. It uses two-dimensional FFT signal processing technology, with a minimum detectable echo power of −170 dBm. For a standard signal source analog echo point frequency, the frequency signal can be broadened into a signal with the same scanning range as the radar system, in order to obtain a calibration curve with the minimum input power of up to −169.77 dBm, in which the calibration curve inflection point is able to confirm that the radar noise power is about −168 dBm. Because the C-FMCW radar has a longer wavelength, the vertical detection range of the C-FMCW radar is greater than that of the MRR. When the C-FMCW radar detects vertically, the attenuation of electromagnetic wave in strong echo area can be ignored. A more detailed algorithm and calibration description of the C-FMCW radar has been described by Ruan [40].

2.2. Case Introduction

In this study, we chose three SCEC cases occurring on 22, 28 June and 11 July 2020. Figure 2 shows the major features of the large-scale conditions during these three mei-yu fronts. In the 500 hPa height field, the middle and high latitudes show a weather situation of “two troughs and one ridge”, and the area south of 30 N is controlled by a subtropical high. On 22 June and 28 June, there was a low vortex over northwest China and the Sea of Japan, and north China was controlled by a northeast–southwest long wave ridge. On 11 July, a long wave ridge developed strongly and formed a blocking high in northeast China. On the 850 hPa wind field, there are shear lines above the target area of the three cases. The southeast wind on the north side of the shear line and the southwest wind on the south side constitute an obvious wind convergence zone, which is conducive to the occurrence and development of clouds. The three SCEC cases were generated against the background of typical mei-yu weather circulation, accompanied by continuous precipitation.
Figure 3 shows profiles of temperature and dew point temperature at Fuyang station, which is the nearest meteorological station to the location of the C-FMCW radar and the MRR. It can be seen from Figure 2 that the precipitable water (Pwat) amounts on 22, 28 June and 11 July are 7 cm, 7 cm, and 8 cm, respectively. Convective Available Potential Energy (CAPE) values are 48 J, 2699 J and 0 J, respectively. This indicates that all three cases have abundant water vaper resources in the atmosphere, and the case on 28 June has more available convective potential energy.

3. Results

3.1. Echo Characteristics Detected by the S-Band Doppler Weather Radar

Due to its volume scanning mode and the wide detection range, the S-band Doppler weather radar data is suitable for analyzing the macro-physical properties of clouds. Figure 4 shows the composite reflectivity of the S-band Doppler weather radar and the vertical cross section using the location of the C-FMCW radar. It can be seen from Figure 4 that the radar echoes occurring on 22, 28 June and 11 July 2020 were obviously unevenly distributed. This indicates that the clouds of the three cases were mainly composed of SCEC. The reflectivity of the S-band Doppler weather radar shows that there are multiple strong echo centers embedded in the stratiform cloud echo in the horizontal direction and multiple strong echo bands embedded in the stratiform cloud echo in the vertical direction. When Marshall (1953) [41] used radar to study the characteristics of precipitation, it was found that there was a band of strong echo from top to bottom in the vertical direction, and it was considered that this strong echo band was related to the convection embedded in the stratiform cloud. Some subsequent studies have confirmed the corresponding relationship between embedded convection and strong echo band [42,43,44,45]. All three cases in our study are SCEC; the embedded convection is the strongest on June 28, with the maximum reflectivity reaching 50 dBZ and the maximum rain rate reaching 51 mm∙h−1, and this is mainly due to the higher CAPE in the atmosphere. The clouds occurring on 22 June and 11 July belong to weak SCEC, with maximum rain rates of 18 mm∙h−1 and 34 mm∙h−1, respectively.

3.2. The CVS of the Three Cases

The C-FMCW radar is suitable for analyzing the detailed vertical structure of cloud due to its high vertical and temporal resolution. Figure 5 is the time series picture of the vertical profile of reflectivity detected by the C-FMCW radar and the rain rate at the height of 100 m detected by the MRR, and the MRR data at 100 m is closest to the ground. It can be seen from the figure that all three SCEC cases obviously have unevenly distributed melting layers, and the thickness range of the melting layer is 100–1200 m. Compared with stratiform clouds, the melting layer of the embedded convection is thicker. In addition, all of embedded convections in these cases exhibit inclined echo bands in the vertical direction, and combined with the wind information presented in Figure 2, it can be seen that the inclination angle of the echo bands is affected by the horizontal wind, and the deformation of the echo band corresponds to the height of the wind shear. The radar reflectivity of the embedded convection is 5–15 dBZ higher than that of the surrounding stratiform clouds. Previous study has shown that the upper region of the embedded convection is rich in supercooled water, and the riming and aggregate process of ice particles in the embedded convection is stronger than that in the surrounding stratiform clouds, resulting in higher precipitation particle concentrations and sizes in the embedded convection, leading to the radar reflectivity also being higher than that of the stratiform cloud [46]. It can also be seen from Figure 5 that the rain rate at the location of the C-FMCW radar is unevenly distributed, and the rain rate of the embedded convection is significantly higher than that of the surrounding stratiform clouds. Previous studies have shown that because the Liquid Water Content (LWC) in embedded convection is higher than that in the surrounding stratiform clouds, and has a certain updraft vertical velocity, more ice crystals can be produced [28,29,31]. The presence of high concentrations of ice crystal and contents supercooled water in embedded convections can improve the precipitation efficiency of stratiform clouds by up to 20–35% [32,33,34]. Qi Peng et al. [47] analyzed the precipitation mechanism of the SCEC in north China using airborne detection data and also found that the embedded convection had a high supercooled water content.

3.3. Vertical Velocity of Particles at Different Altitudes

Figure 6 shows the vertical velocity of particles at different altitudes detected using the C-FMCW radar. It can be seen from the figure that the three cases have a common feature, which is that the vertical velocity of particles has a rapid increasing trend near the melting layer in stratiform regions. The vertical velocity of particles in the upper part of the melting layer is generally in the range of 0–4 m·s−1, and increases rapidly to 4–12 m·s−1 near the bottom of the melting layer. However, the vertical velocity of particles in the embedded convection is inconsistent with that in the stratiform regions, and especially in the case of 28 June, the particle vertical velocity distribution in the embedded convection is very uneven due to the vigorous development of embedded convection. In addition, the three cases have another common feature, which is that the vertical velocity of particles has a decreasing trend in the lower layer of the stratiform regions, while that of the embedded convection with higher rain rate has no obvious decreasing trend. Our cases present similar results to those of Peters et al. [38], who analyzed the precipitation characteristics near the Baltic Sea, and found that the particle falling velocity decreased or remained unchanged under cloud with light rain intensity, but the particle falling velocity increased under cloud with a rain intensity range of 20–200 mm·h−1.
Figure 7 shows the vertical velocity of particles detected by the C-FMCW radar on 28 June. It can be seen from the figure that, due to the vigorous development of embedded convection, the particles in the upper region of embedded convection show an upward movement, and the maximum upward velocity reaches 5 m·s−1. This phenomenon has also been observed using airborne instruments from the SCEC in north China [47]. This is mainly due to the air, with large convergence and rising speed in the embedded convection, being able to drive the cloud particles to rise. As can be seen from the enlarged picture in Figure 7, there is a high-falling-speed region near the particle rising region, indicating that there are larger high-density precipitation particles, such as hail or heavy rimmed particles, present in the cloud. Therefore, we speculate that the growth rate of particles in the upper region of the embedded convection is faster than that in the stratiform regions. Zhu et al. [47] analyzed the ice particles of the SCEC in north China by using airborne detection data and found that the ice particles in the embedded convection had a faster growth rate due to the stronger riming and aggregate process. He Hui et al. [48] used WRF combined with the trajectory model to simulate the precipitation mechanism of the SCEC in north China, and found that the cloud particles in the embedded convection were driven to move up and down, and then grow larger.

3.4. Vertical Distribution of Drop Spectrum Distributions (DSDs)

Figure 8 shows the vertical distribution of the average DSDs of the three cases detected by the MRR. It can be seen from Figure 8 that all three cases have some common features; for example, the average number concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km. As can be seen from Figure 2, the LCLs of the three cases are 0 m, 600 m, and 0 m, respectively, so all falling processes are carried out in the cloud. The main reasons for this change should be that the collision–coalescence of drops is dominant at 3–1 km. It can be seen from Figure 2 that south or southeast wind is present at 3–1 km in these cases, bringing an abundance of water vapor from the sea to the study area. This water vapor can condense more small drops, increasing the efficiency of the collision–coalescence process. In addition, for the case on 22 June, the average number concentration of drops larger than 4 mm decreased as they fell from 1 km to ground, but the average number concentration of drops smaller than 0.5 mm increased, indicating that the breakup of large drops is dominant at this stage. Cui et al. [49] analyzed the vertical distribution of the DSDs of the clouds over north China using MRR and found that the average number concentration of small drops decreased while that of medium and large drops increased, mainly due to the strong collision–coalescence process outside the cloud. Wang et al. [50] analyzed the vertical distribution of DSDs of different types of precipitation in north China using MRR, and found the concentration of large raindrops decreased with decreasing height when the rain intensity was less than 2 mm·h−1, and increased when the rain intensity was greater than 2 mm·h−1.
Figure 9 presents a comparison of the vertical distribution of average DSDs between embedded convection and stratiform cloud. To investigate the difference in the vertical distribution of DSDs between stratiform cloud and embedded convection, taking the case of July 11 as an example, the embedded convection stage (T1) and stratiform cloud stage (T2) are selected for comparison. It can be seen from Figure 9 that the concentration of drops (0.5–5 mm) in the embedded convection is greater than that in the stratiform cloud near the ground. With the decrease in height in the embedded convection, although the concentration of small drops changes little, the average concentration of particles with size greater than 2 mm increases significantly. However, in the T2 stage, the average concentration of drops smaller than 0.5 mm increases, and the average concentration of drops larger than 2.5 mm decreases. Therefore, we speculated that the collision–coalescence process of the drops in the embedded convection is stronger than that in the stratiform region.

4. Discussion and Conclusions

In this study, we analyzed the CVS of the three SCEC cases occurring in the mei-yu fronts over central-eastern China on 22, 28 June and 11 July 2020 based on the combination of three types of radar. It is found that the clouds during the three mei-yu processes are mainly composed of SCEC, showing that the echo of the stratiform clouds is embedded with multiple strong echo centers in the horizontal direction, and there are strong echo bands embedded in the stratiform cloud echo in the vertical direction.
By analyzing the C-FMCW radar data, it is found that there are obvious melting layers in the three SCEC cases; the melting layers are unevenly distributed, and the thickness range of the melting layers is 100–1200 m. The embedded convection shows an inclined echo band in the vertical direction, and the radar reflectivity is 5–15 dBZ greater than the surrounding stratiform regions. The rain rate at the location of the C-FMCW radar is unevenly distributed, and the rain rate of the embedded convection is higher than that of the surrounding stratiform clouds. This corresponds to the heavy precipitation center at the bottom of the vertical echo band.
In the stratiform regions, the vertical velocity of particles in the upper part of the melting layer is generally in the range of 0–4 m·s−1, and increases rapidly to 4–12 m·s−1 near the bottom of the melting layer. However, the vertical velocity of the particles in the embedded convection is inconsistent with the stratiform regions, and especially in the case of 28 June, the particle vertical velocity distribution in the embedded convection is very uneven due to the vigorously development of embedded convection. In addition, the three cases have another common feature, which is the vertical velocity of particles has a decreasing trend in the lower layer of stratiform regions, while that of embedded convection has no obvious decreasing trend. In the case on 28 June, due to the vigorous development of embedded convection, the cloud particles in the upper layer show an upward movement, and there is a high falling speed appearing near the particle rising region, indicating that there are larger high-density precipitation particles, such as hail or heavy rimmed particles, present in the cloud. Therefore, we speculate that the growth rate of particles in the upper region of the embedded convection is higher than that in the stratiform regions.
By analyzing the MRR data, it is found that the DSDs in the three cases have common variation characteristics in the vertical direction. Due to the collision–coalescence of drops, the average number concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km. In addition, the comparison of the vertical distribution of DSDs between embedded convection and stratiform cloud shows that the collision–coalescence process of the drops in the embedded convection is stronger than that in the stratiform region.

Author Contributions

Conceptualization, S.Z.; Methodology, S.Z.; resources, Y.Y.; software and data curation, Y.W.; review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2019YFC1510303); National Natural Science Foundation of China (41875171); Key Research and Development Project in Anhui Province (1704f0804055); LCP/CMA (2018Z01615).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 13 01088 g001 550
Figure 1. (a) Eastern China topography (color shading represents elevation, red box shows study area). (b) The locations of three radars and Fuyang station in the study area (red circle represents the S-band Doppler weather radar coverage; red cross symbol represents the S-band Doppler weather radar; red dot symbol represents the C-FMCW radar and the MRR; red box symbol represents Fuyang station).
Figure 1. (a) Eastern China topography (color shading represents elevation, red box shows study area). (b) The locations of three radars and Fuyang station in the study area (red circle represents the S-band Doppler weather radar coverage; red cross symbol represents the S-band Doppler weather radar; red dot symbol represents the C-FMCW radar and the MRR; red box symbol represents Fuyang station).
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Figure 2. Geopotential height at 500 hPa and horizontal wind barbs at 850 hPa (the location of the black spot is the study area). (a) 1400 LST 22 June; (b) 0200 LST 28 June; (c) 1400 LST 11 July.
Figure 2. Geopotential height at 500 hPa and horizontal wind barbs at 850 hPa (the location of the black spot is the study area). (a) 1400 LST 22 June; (b) 0200 LST 28 June; (c) 1400 LST 11 July.
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Figure 3. Profiles of temperature (black line) and dew point temperature (blue line) at Fuyang station (red dotted line represents the pseudoadiabat curve). (a) 1300 LST 22 June; (b) 1900 LST 27 June; (c) 1300 LST 11 July.
Figure 3. Profiles of temperature (black line) and dew point temperature (blue line) at Fuyang station (red dotted line represents the pseudoadiabat curve). (a) 1300 LST 22 June; (b) 1900 LST 27 June; (c) 1300 LST 11 July.
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Figure 4. Reflectivity of the S-band Doppler weather radar (the black cross symbol represents the location of the C-FMCW radar and the MRR). (a) Composite reflectivity at 1706 LST on 22 June; (b) vertical cross section along black line in figure (a); (c) composite reflectivity at 0336 LST on 28 June; (d) vertical cross section along black line in figure (c); (e) Composite reflectivity at 1506 LST 11 July; (f) vertical cross section along black line in figure (e).
Figure 4. Reflectivity of the S-band Doppler weather radar (the black cross symbol represents the location of the C-FMCW radar and the MRR). (a) Composite reflectivity at 1706 LST on 22 June; (b) vertical cross section along black line in figure (a); (c) composite reflectivity at 0336 LST on 28 June; (d) vertical cross section along black line in figure (c); (e) Composite reflectivity at 1506 LST 11 July; (f) vertical cross section along black line in figure (e).
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Figure 5. (a) The C-FMCW radar reflectivity on 22 June; (b) rain rate at the height of 100 m on 22 June detected by the MRR; (c) the C-FMCW radar reflectivity on 28 June; (d) same as (b), rain rate on 28 June; (e) the C-FMCW radar reflectivity on 11 July; (f) same as (b), rain rate on 11 July.
Figure 5. (a) The C-FMCW radar reflectivity on 22 June; (b) rain rate at the height of 100 m on 22 June detected by the MRR; (c) the C-FMCW radar reflectivity on 28 June; (d) same as (b), rain rate on 28 June; (e) the C-FMCW radar reflectivity on 11 July; (f) same as (b), rain rate on 11 July.
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Figure 6. The vertical velocity of particles detected by the C-FMCW radar (positive velocity is downward) (a) 22 June; (b) 28 June; (c) 11 July.
Figure 6. The vertical velocity of particles detected by the C-FMCW radar (positive velocity is downward) (a) 22 June; (b) 28 June; (c) 11 July.
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Figure 7. The vertical velocity of particles detected by the C-FMCW radar on 28 June (the arrow in the enlarged picture indicates the direction of particles motion).
Figure 7. The vertical velocity of particles detected by the C-FMCW radar on 28 June (the arrow in the enlarged picture indicates the direction of particles motion).
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Figure 8. Vertical distribution of average DSDs detected by the MRR (a) 1640–1800 LST on 22 June (the left margin in the figure is the missing observation data, which also appears in the other DSDs figure); (b) 0300–0400 LST on 28 June; (c) 1510–1620 LST on 11 July.
Figure 8. Vertical distribution of average DSDs detected by the MRR (a) 1640–1800 LST on 22 June (the left margin in the figure is the missing observation data, which also appears in the other DSDs figure); (b) 0300–0400 LST on 28 June; (c) 1510–1620 LST on 11 July.
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Figure 9. Comparison of vertical distribution of average DSDs between embedded convection and stratiform cloud. (a) The CVS on 11 July; (b) the vertical distribution of the DSDs in the T1 time period in (a); (c) vertical distribution of the DSDs in the T2 time period in (a).
Figure 9. Comparison of vertical distribution of average DSDs between embedded convection and stratiform cloud. (a) The CVS on 11 July; (b) the vertical distribution of the DSDs in the T1 time period in (a); (c) vertical distribution of the DSDs in the T2 time period in (a).
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Table
Table 1. Main parameters of the S-band Doppler weather radar, the C-FMCW radar, and MRR.
Table 1. Main parameters of the S-band Doppler weather radar, the C-FMCW radar, and MRR.
Radar ParametersDoppler Weather RadarC-FMCWMRR
Wavelength10 cm5.4 cm1.25 cm
Detection modeVolume scanningVertical scanningVertical scanning
Detection range300 km (horizontal), 20 km
(vertical)		150 m–9 km0–3.1 km
Temporal resolution6 min3 s1 min
Vertical resolution1 km30 m100 m
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Zhu, S.; Yuan, Y.; Wu, Y.; Zhang, Y. Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front. Atmosphere 2022, 13, 1088. https://doi.org/10.3390/atmos13071088

AMA Style

Zhu S, Yuan Y, Wu Y, Zhang Y. Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front. Atmosphere. 2022; 13(7):1088. https://doi.org/10.3390/atmos13071088

Chicago/Turabian Style

Zhu, Shichao, Ye Yuan, Yue Wu, and Ying Zhang. 2022. "Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front" Atmosphere 13, no. 7: 1088. https://doi.org/10.3390/atmos13071088

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