In previous aircraft observation studies, the definition of the in-cloud sample was based on the thresholds of Nc and/or LWC (Rangno and Hobbs, 2005; Noh et al., 2013; O'Shea et al., 2017; D'Alessandro et al., 2021; Huang et al., 2021). In our study, a 1-s in-cloud sample in MPTR was defined if either of the following two conditions was met: (1) MFCDP was greater than 0.001 g m−3 or (2) the number concentration of particles larger than 50 μm from 2DS (N2DS>50) was greater than 1 L−1. The threshold of MFCDP used in our study is similar to the studies listed above, which was found suitable in North China for in situ observation (Zhao et al., 2018). Also, the threshold of N2DS>50 was used to determine the existence of large supercooled droplets and/or ice particles (D'Alessandro et al., 2021). Meanwhile, the altitude of the in-cloud sample had to be higher than 100 m above ground to remove the possible influence of fog or haze.
The cloud phase was determined from FCDP and 2DS separately after selecting the in-cloud samples and removing the noise (Fig. 2). Previous studies have suggested that the small particles from FCDP were usually liquid if the number concentration of FCDP (NFCDP) was greater than 1 cm−3 (Lance et al., 2010; D'Alessandro et al., 2021). Therefore, the threshold value of NFCDP ≥ 1 cm−3 was used to determine whether the particles of FCDP were liquid in our study. When NFCDP ≥ 1 cm−3, the phase of 2DS was determined using the “μ-method”, which was similar to Korolev et al. (2003). In our study, we calculated the LWC and IWC of 2DS using the method in section 2.3, and μ equals the ratio of IWC to the sum of LWC2DS and IWC. The phase of 2DS was regarded as ice if NFCDP ≤ 1 cm−3 since the absence of small supercooled droplets usually meant that there were no large supercooled droplets. The phase of each in-cloud sample was the combination of FCDP and 2DS (e.g., FCDP=liquid and 2DS=liquid were classified as liquid, FCDP=liquid and 2DS=mixed were classified as mixed, etc.). After determining the phase of the samples, we checked the 2DS images of the in-cloud samples. We found ice particles and/or liquid droplets for different cloud types, which suggested that the criterion for determining phase was suitable in our study.
Using this method, we have collected 69850 in-cloud 1-s samples in MPTR over the 22 flights mentioned in section 2.1. The detailed information regarding these flights is shown in Table 1, including the flight time, synoptic type, in-cloud samples in MPTR, temperature range of in-cloud samples, the fraction of ice, mixed-phase, and liquid clouds for each flight, and the air quality data. With the temperature decreasing, the number of in-cloud samples increased above –10°C and decreased at lower temperatures (Fig. 3). For all in-cloud samples in MPTR, the average fraction of liquid, mixed-phase, and ice cloud samples was 4.9%, 23.3%, and 71.8%, respectively. The relative fraction changed with temperature as well (Fig. 3). Ice fraction was larger than 30% at all MPTR, reaching 90% below –30°C. Liquid and mixed fractions decreased with temperature decreasing, but liquid samples were still found at temperatures near −35°C. The fraction of different cloud types varied significantly from flight to flight, with the fraction of ice ranging from 5.3% to 99.2%, the fraction of mixed-phase ranging from 0.3% to 62.6%, and the fraction of liquid ranging from 0% to 91.3%. The difference can be attributed to the difference in sampling temperature between flights.
Date Time (LST) Synoptic type
(Upper air +
in MPTR (s)
Temperature range (°C) Ice (%) Mixed (%) Liquid (%) AQI_
2014/02/28 1240–1610 Trough + high
2744 –15.4– –1.0
63.8 29.1 7.1 103 78 2015/01/14 1410–1640 Trough + high
5350 –13.2– –0.7
69.5 29.5 1.0 264 214 2015/01/25 1000–1230 Trough + high
1046 –32.8– –3.0
75.0 19.9 5.1 178 134 2015/11/05 1210–1630 Ridge + high
6695 –36.3– –0.1
79.4 19.2 1.4 144 110 2015/11/11 1308–1620 Weak trough +
407 –4.0– –0.1
6.1 34.4 59.5 137 104 2015/11/17 1730–2020 Weak trough +
5153 –11.5– –3.2
94.7 0.3 5.0 82 60 2016/01/16 0958–1145 Trough + easterly
120 –20.3– –8.9
8.3 43.3 48.4 162 123 2016/01/16 1820–2145 Trough + easterly
6460 –39.8– –8.8
63.2 35.4 1.4 162 123 2016/01/21 0730–0935 Weak trough +
367 –24.2– –7.0
50.6 29.2 20.2 173 131 2016/11/29 1205–1545 Trough + high pressure 9841 –35.2– –5.7
73.7 26.3 0 129 98 2016/12/12 1245–1620 Trough + high
417 –12.3– –0.9
99.2 0.8% 0 271 221 2017/02/07 0939–1156 Trough + high
5872 –35.7– –1.2
60.7 35.5 3.8 103 80 2017/11/24 1541–1818 Trough + low
2652 –24.8– –3.9
69.5 24.2 6.3 47 22 2020/01/05 1528–1718 Ridge + high
2122 –25.5– –5.5
95.1 0.2 4.7 83 61 2020/12/28 0954–1300 Weak trough +
3547 –39.8– –3.3
98.9 0.4 0.7 77 56 2021/01/14 0938–1240 Trough + low
2655 –36.4– –3.0
66.4 27.3 6.4 80 53 2021/01/14 1600–1850 Trough + low
1823 –39.8– –0.7
97.2 1.8 1.0 80 53 2021/01/25 1038–1142 Trough + high
2338 –12.6– –1.6
75.4 22.9 1.7 176 133 2021/01/27 1538–1725 Trough + low
4454 –24.0– –0.5
37.2 62.6 0.2 55 26 2021/02/23 0958–1225 Trough + high
3506 –39.8– –2.6
95.5 3.5 1.0 49 28 2021/02/27 1614–1840 Ridge + high
1452 –10.6– –3.5
5.3 3.4 91.3 159 121 2021/02/28 1041–1228 Ridge + easterly
829 –26.5– –0.1
48.1 25.0 26.9 125 95 Total – – 69850 –39.8– –0.1
71.8 23.3 4.9 – –
Table 1. The summary of meteorological conditions, flight information, and pollution index for the flights used in this study. The numbers in parentheses are the mean values (LST = UTC + 8).
Figure 3. The relationship between temperature and the number of in-cloud samples (black line) and the relative fraction of liquid (green line), mixed-phase (red line), and ice (blue line) samples.
Figure 4 shows the probability density function (PDF) of the ice mass fraction (IMF), i.e., the ratio of IWC to total water content (TWC). Here, IWC and LWC were calculated by the method shown in Fig. 2, and TWC equaled the sum of IWC and LWC. Previous studies found a U-shaped distribution of IMF for cloud samples in MPTR (e.g., Korolev et al., 2003). Compared to the studies listed above, the probability of IMF < 0.1 was much smaller while the probability of IMF > 0.9 was much larger, making the U-shape become a half-U-shape. With the temperature decreasing, the probability of IMF < 0.1 decreased as well. The lower fraction of liquid cloud and the small probability of small IMF indicated that ice clouds dominated MPTR, while liquid clouds were rare in winter over north China. Because the mixed-phase clouds have a strong relationship with the rainfall and snowfall in winter, the discussion in the following sections is focused on mixed-phase clouds.
The statistical results of microphysical properties for mixed-phase samples, including Nc, LWC, Ni, IWC, TWC, the effective diameter of cloud droplets (Dc) and ice particles (Di), are shown in Table 2, including the median values, the mean values, the standard deviations, and the coefficient of variations (CV, equaling to the ratio of standard deviation to mean). In our study, the effective diameter of both liquid droplets and ice particles was defined as the ratio of the third to the second moment of the PSDs.
Cloud Microphysical Properties Median Value Mean Value Standard Deviations Coefficient of Variation Nc (cm−3) 1.8 43.9 152.0 3.45 LWC (g m−3) 0.015 0.032 0.059 1.85 Dc (μm) 10.35 12.45 7.24 0.58 Ni (L−1) 27.2 42.3 44.2 1.04 IWC (g m−3) 0.094 0.136 0.173 1.27 Di (μm) 413.87 436.61 171.28 0.39 TWC (g m−3) 0.122 0.168 0.193 1.15
Table 2. The statistical results regarding cloud microphysical properties for mixed-phase cloud samples.
For wintertime mixed-phase cloud samples, the average Nc and Ni were 43.9 ± 152.0 cm−3 and 42.3 ± 44.2 L−1, respectively; the average LWC, IWC, and TWC were 0.032 ± 0.059, 0.136 ± 0.173, and 0.168 ± 0.193 g m−3, respectively; the average Dc and Di were 12.45 ± 7.24 μm and 436.61 ± 171.28 μm, respectively. The median values of cloud microphysical properties were smaller than the mean values, meaning that extremely large values existed in the datasets, indicating the complexity and variability of microphysical processes in mixed-phase clouds.
When comparing the CV of the microphysical properties (Table 2), we found that the CV of liquid-phase properties (Nc, LWC, and Dc) was larger than the corresponding ice-phase properties (Ni, IWC, and Di), meaning that the liquid-phase properties were more variable. The larger CV for liquid properties differed from previous results (Gultepe et al., 2002; Lachlan-Cope et al., 2016), which found that CV for liquid properties was smaller than ice properties. But this is not surprising because the threshold of Nc (NFCDP) in this study was smaller than those. The smaller threshold of Nc contained a large variation range of Nc, which represented the different development stages for mixed-phase clouds, from highly glaciated to highly supercooled. If we use Nc ≥ 10 cm−3 as the threshold for liquid samples of FCDP, we would get a smaller CV for liquid properties (Table A1), which is more like the studies listed above.
The comparisons of cloud microphysical properties of wintertime mixed-phase cloud between this study and different mid-latitude regions are shown in Fig. 5, including the Southern Ocean (SO) [Ahn et al., 2017 (SO17); Wang et al., 2020 (SO20)], North America (NA) [Cober et al., 2001 (NA01); Fleishauer et al., 2002 (NA02)], and East Europe (EE) [Korolev et al., 2001 (EE01)]. Due to the differences in probes, the measurements of ice particles were different, while the measurements of liquid droplets were basically the same. As a result, we only compared the liquid-phase properties between different studies. However, the Nc thresholds of in-cloud samples differed in these studies, and some studies did not specify the thresholds of Nc. To better compare the difference, we used the results with the Nc threshold of 1 and 10 cm−3 (as shown in Table 2 and Table A1, respectively) for the comparison (the two different bars of “North China” in Fig. 5). The results with the Nc threshold of 10 cm−3 were a subset of our dataset. From Table 2 and Table A1, we found that the results were sensitive to the Nc thresholds, and the mean and median values of Nc and LWC became larger with larger Nc thresholds. Compared to the results of mixed-phase clouds from other mid-latitude regions, we found that Nc in our study was larger, while LWC and Dc were smaller. Though some studies (NA02 and SO20) did not provide Dc, we could estimate the range of Dc using formula (1) that Dc should be larger when Nc was smaller and LWC was larger. After this estimation, we found that the Dc of our research was smaller than these studies as well.
Figure 5. The comparison of cloud microphysical properties (Nc, LWC, and Dc) of mixed-phase cloud samples between our study (North China) and previous studies (SO20: Wang et al., 2020; SO17: Ahn et al., 2017; NA02: Fleishauer et al., 2002; NA01: Cober et al., 2001; EE01: Korolev et al., 2001). The Nc thresholds for in-cloud samples are shown in the figure as well.
According to Martin et al. (1994), the width of the cloud droplet distribution (for droplets between 2 to 50 μm) could be described by parameter k, which is the ratio of the third power of mean volume diameter to the third power of the effective diameter. From this definition, smaller k usually indicates broader droplet distributions. After calculation, we found that k was the smallest in this study, suggesting that the cloud droplet distribution was wider than in other regions (Table 3). Our small k was consistent with previous studies, which found that k for the continental or polluted clouds was smaller than maritime or clean clouds (Yum and Hudson, 2004).
Study North China SO17 EE01 NA01 k 0.56 0.66 0.69 0.64
Table 3. The value of k for mixed-phase cloud samples in this study (North China) compared with previous studies (SO17: Ahn et al., 2017; NA01: Cober et al., 2001; EE01: Korolev et al., 2001).
The larger Nc and smaller Dc are consistent with the severe pollution caused by human activities in North China in winter (Liang et al., 2018). Since the observation of aerosols from aircraft was missing in most flights, we used the air quality data provided by the China National Environmental Monitoring Centre to verify the degree of pollution. The air quality index (AQI) was used to determine the polluted day if daily mean AQI > 100. We found that 13 of these 20 days for the flights were polluted days, and the average PM2.5 for the 20 days was 97.4 μg m−3 in Beijing. This mass concentration was higher than the class 2 limit for the daily mean value and was much higher than the annual mean value in Beijing. Under severe pollution conditions, there existed an increase in Nc but a decrease in Dc compared to clean conditions, resulting in broader cloud droplet distribution. We found the larger values of Nc in different flights, which confirmed the common presence of large aerosol loading. The larger Nc and smaller Dc results are consistent with Jackson et al. (2012), suggesting that the clouds sampled in more polluted conditions had larger Nc but smaller Dc. The smaller LWC is consistent with the lower temperature during flights and the difference in air mass type. The average temperature in our study was lower than the other studies in comparison. Previous studies have found that LWC decreased slightly with temperature decreasing (Gultepe et al., 2002); thus, the LWC in our study was smaller. Besides, the LWC from continental studies (EE01, NA01, NA02, and this study) were also smaller than the maritime studies (SO17, SO20), which was same as the previous study about the difference in airmass type (Martin et al., 1994; Yum and Hudson, 2001).
It's essential to know how the statistical distributions of cloud microphysical properties vary with temperature, both for model simulation and remote sensing inversion. Since the different observation flights in our study were carried out at different altitudes and temperatures, we analyzed the relationship between temperature and cloud microphysical properties to obtain the statistical distributions. In our study, the mixed-phase cloud samples appeared at all ranges of MPTR. Figure 6 shows box plots of mean values (red dots), median values (blue dots), 25th and 75th percentiles for Nc, LWC, Dc, Ni, IWC, and Di. We averaged these properties into 2.5°C temperature bins for further analysis.
From Figs. 6a–c, it can be seen that Nc and LWC increase with increasing temperature, but the Dc decreases. The peak value of Nc appears at ~ –8°C, while the peak value of LWC appears at ~ –20°C. The range of variations of Nc and LWC in one temperature bin also increases when temperature increases. Still, the variation range of Dc in one temperature bin does not change much, and the outliers of Dc are less than Nc and LWC, which is consistent with the result that the CV of Dc is smaller than Nc and LWC. The increasing Nc and LWC with temperature increase is consistent with previous studies for wintertime mixed-phase cloud samples (Gultepe et al., 2002; Korolev et al., 2003; Noh et al., 2013).
Figure 6. Box plots as a function of temperature for (a) Nc, (b) LWC, (c) Dc, (d) Ni, (e) IWC, and (f) Di for mixed-phase cloud samples. The red dots represented mean values; the blue dots represented median values; and the left and right sides of the box indicated the 25th and 75th percentiles, respectively. The whisker's length is 1.5IQR (IQR, the interquartile range, equal to the difference between 75th percentiles and 25th percentiles). The red crosses represented the outliers, which refer to a value more than 1.5IQR away from the bottom or top of the box.
From Figs. 6d–f, it can be seen that Ni decreases, but the Di increases with temperature. In general, the larger values of Ni correspond to smaller Di. What's more, with temperature increasing, the range of variation of Ni in one temperature bin decreases. In comparison, the variation range of Di in one temperature bin increases, which is opposite to the liquid-phase properties. Unlike LWC, the IWC exhibited poor correlation with temperature, and the variation range in one temperature bin becomes larger when IWC is larger. The main reason for the poor correlation between IWC and temperature is the complicated ice-phase microphysical process (e.g., riming, aggregation, deposition, and WBF process) in mixed-phase clouds (Morrison et al., 2020). These microphysical processes could make ice particles more variable in shape and number at the same temperature, as the CV showed in Table 1. The decrease of Ni and the increase of Di with increasing temperature are similar to previous studies for mixed-phase clouds in midlatitude regions (Fleishauer et al., 2002; Carey et al., 2008; Noh et al., 2013).
In conclusion, the statistical distributions of the variance of mixed-phase cloud microphysics with temperature are similar to other mid-latitude regions, although there exist some difference in statistical results of the cloud microphysical properties.
We next discuss the mixed-phase cloud samples at different temperatures. In order to better analyze the microphysical processes, the variation of PSDs and ice habits are discussed first. The whole dataset was divided into four intervals according to the cumulative distribution function (CDF) of temperature: –10°C–0°C, –20°C– –10°C, –30°C– –20°C, and –40°C– –30°C. We calculated the average PSD and number concentration in each interval. Figure 7 shows the variation of PSDs for mixed-phase cloud samples at different temperature intervals.
Figure 7. The combined PSDs for mixed-phase cloud samples from FCDP and 2DS. The red lines are the average PSDs of liquid droplets, which combine the PSDs from FCDP and the liquid droplets from 2DS, and the blue lines are the average PSDs of ice particles from 2DS.
The liquid PSDs are unimodal for different temperature intervals. As the temperature increases, the peaks of liquid PSDs remained unchanged (at around 5 μm), while the concentration of the peaks increases, which means there exist more small droplets at warmer temperatures. With the temperature rising, the number concentration of liquid droplets with diameters between 10 μm and 30 μm increases. The increase of small droplets with temperature is consistent with the Nc shown in Fig. 6a. Though the number concentration of liquid droplets with diameters larger than 100 μm decreases at higher temperatures, their existence still indicates the common presence of drizzle at different temperatures.
The ice PSDs are generally bimodal, but this feature becomes less evident at higher temperatures. The ice PSDs exhibits the first peak at 80 μm. The peaks remain unchanged, but the number concentration of the peaks decreases as the temperature increases. The second peaks remain unchanged at around 200 μm, and the number concentration of these peaks decreases with temperature increasing. The decreasing of peaks of ice PSDs is consistent with the decreasing of Ni with temperature increasing, as shown in Fig.6d. The maximum diameter of the ice PSDs and the number concentration of ice particles with diameters larger than 600 μm become larger as the temperature increases. In comparison, the number concentration of ice particles smaller than 600 μm becomes smaller. Therefore, the Di increases slightly with temperature increasing, as shown in Fig. 6f.
Images of hydrometeors of mixed-phase cloud samples from 2DS in different temperature intervals are shown in Fig. 8. These images were selected from different flights at the temperature intervals to be more representative. The coexistence of droplets and ice particles is found at all temperature intervals from the images of 2DS, though the droplets smaller than 10 μm cannot be recorded due to the resolution. Drizzle-sized drops are found at different temperatures, which confirms the inference in liquid PSDs. Though the supersaturation data was missing, we still found that the relationship between temperature and ice habit is basically the same as the habit diagram described by Bailey and Hallett (2009), i.e., we found that below –30°C (Fig. 8a), ice particles were mainly plates, columns, and column combinations, and the volume of ice particles was relatively small, and that with the temperature increasing, the ice particles become larger. What's more, larger irregular ice particles appear at higher temperatures (e.g., Figs. 8b, 8c, and 8d). Above –20°C, the ice particles were mostly plates, stellar crystals, irregular crystals, and capped columns (Figs. 8c and 8d), and the ice particles became larger. The aggregates of ice particles could be clearly observed above –10°C (e.g., Figs. 8d1, 8d2, and 8d3). The results of the ice particle size from 2DS images are consistent with the change of Di shown in Fig. 6f.
Figure 8. Sampled images of mixed-phase cloud hydrometeor samples from 2DS under different temperature conditions. The images were selected from different flights in the corresponding temperature intervals, and the width of each 2DS channel is 1280 μm.
The riming process refers to the growth of an ice particle by collision with supercooled cloud droplets. In contrast, the aggregation process clumps ice particles together to grow larger (Morrison et al., 2020). Previous studies have found that the aggregation and riming processes are two critical processes in mixed-phase clouds (Zhu et al., 2015; Lohmann et al., 2016; Taylor et al., 2016). From the analysis of the cloud properties, PSDs, and ice habits at different temperatures above, we could infer that the results are consistent with the operation of the aggregation and riming processes, and that the activity of these physical processes was different at different temperatures. We found that these two processes were not significant from the shape and number concentration of ice crystals at lower temperatures. As the temperature increased, the number concentration of liquid droplets larger than 100 μm reduced. This is consistent with the notion that large droplets were rimmed to ice particles, making the riming process more pronounced. At higher temperatures, especially above –10°C (e.g., Figs. 8d1, 8d2, and 8d3), there existed obvious aggregates of ice particles: Di became larger while Ni became smaller, and more large ice particles existed. This suggests that the larger particles came from the clumping of small ice particles. All these results are consistent with the aggregation process becoming more active at higher temperatures.
Using aircraft observational data, we investigated the statistics of the microphysical characteristics of wintertime cold clouds in North China for the first time. The flights were carried out in winters from 2014 to 2017, 2020, and 2021, and the in-cloud data was 69,850 s in total.
For all in-cloud samples in mixed-phase temperature ranges (MPTR, –40°C to 0°C), the average fraction of liquid, mixed-phase, and ice cloud samples was 4.9%, 23.3%, and 71.8%. The ice fraction increases with decreasing temperature and reaches 90% below –30°C, while liquid and mixed fraction decrease. The probability of ice mass fraction (IMF) showed a "half-U-shape" with a small probability of IMF < 0.1 and a large probability of IMF > 0.9. This indicates that ice clouds dominated MPTR, while liquid clouds were rare in winter.
For wintertime mixed-phase cloud samples, the average Nc and Ni were 43.9 ± 152.0 cm−3 and 42.3 ± 44.2 L−1, respectively; the average LWC, IWC, and TWC were 0.032 ± 0.059, 0.136 ± 0.173, and 0.168 ± 0.193 g m−3, respectively; the average Dc and Di were 12.45 ± 7.24 μm and 436.61 ± 171.28 μm, respectively. Compared to the observation for mixed-phase clouds results in winter in other mid-latitude regions, we found that Nc was larger while LWC and Dc were smaller in North China, resulting in the broader cloud droplet distribution. The larger Nc and smaller Dc are consistent with the existence of heavy pollution in winter in North China. The smaller LWC occurred during the lower temperature flights and change in air mass type.
The relationships between temperature and different cloud microphysical properties varied: Nc, LWC, and Di increased as the temperature increased, but Ni and Dc decreased, and IWC exhibited a poor correlation with temperature. The relationship between temperature and cloud microphysical properties is similar to that found in previous studies in other mid-latitude regions, indicating that the temperature dependence of cloud microphysical properties was similar for mixed-phase clouds.
The liquid PSDs were unimodal, while the ice PSDs were bimodal. With increasing temperature, the peak of liquid PSDs remained unchanged, but the number concentration of the peaks increased. For ice PSDs, as the temperature increased, the first peaks remained unchanged, but the number concentration of these peaks decreased; the second peaks gradually shifted to larger sizes, and the number concentration of the peaks decreased.
The ice habit at different temperatures was basically the same as the description from Bailey and Hallett (2009). Ice particles were mainly plates and columns, with aggregates observed at lower temperatures, and small volumes of ice particles. At higher temperatures, the ice particles were mostly plates, stellar crystals, irregular crystals, and capped columns, and the particles became larger.
Our results are consistent with the operation of the aggregation and riming processes. The relative importance of these processes varied significantly at different temperatures, though both aggregation and riming processes were more active at higher temperatures.
This work fills the gap in the aircraft observation research of wintertime cold clouds in North China. This may also be helpful in the development of remote sensing retrieval algorithms and microphysical schemes in model simulations. The results suggested that ice clouds dominated MPTR, while liquid clouds were rare in winter in North China. Though the wintertime mixed-phase clouds had some unique microphysical characters in North China, the temperature dependence of cloud properties was basically consistent with previous results in other regions in winter. Besides, the dominant ice-phase microphysical processes in wintertime mixed-phase clouds were the aggregation and riming processes. However, due to the air traffic control and the risk of aircraft icing in winter, the flights of aircraft observations were fewer than in other seasons. The microphysical properties of wintertime cold clouds need to be thoroughly investigated under different weather conditions by aircraft observations in North China in the future. What's more, it is necessary to combine other observation methods such as satellite, lidar, and radar with aircraft observations to gain a more comprehensive understanding of the microphysical structure of these clouds.
Acknowledgements. This work is supported by the National Natural Science Foundation of China (Grant Nos. 41925023, 91744208, 41575073, 41621005, and 42075084) and by the Ministry of Science and Technology of the People's Republic of China (Grant Nos. 2017YFA0604002 and 2016YFC0200503). This research is also supported by the Collaborative Innovation Center of Climate Change, Jiangsu Province.
Cloud Microphysical Properties Median Value Mean Value Standard Deviations Coefficient of Variation Nc (cm−3) 130.8 231.0 288.6 1.25 LWC (g m−3) 0.038 0.076 0.114 1.51 Dc (μm) 8.31 9.16 3.74 0.41 Ni (L−1) 7.9 24.2 37.9 1.57 IWC (g m−3) 0.022 0.108 0.274 2.54 Di (μm) 414.51 448.73 221.26 0.49 TWC (g m−3) 0.090 0.183 0.312 1.71
Table A1. Same as Table 1, but the threshold for liquid phase of FCDP is 10 cm−3.
Figure A1. Comparison between the Hotwire LWC (LWCHotwire) and estimated LWC (LWCest, the sum of LWC from FCDP and 2DS) on the King Air 350 aircraft. The red line is the linear best fit line with a correlation coefficient of 0.88. The calibration was carried out by the Beijing Weather Modification Office staff in their laboratory.
|Date||Time (LST)||Synoptic type|
(Upper air +
|In-cloud time |
in MPTR (s)
|Temperature range (°C)||Ice (%)||Mixed (%)||Liquid (%)||AQI_|
|2014/02/28||1240–1610||Trough + high |
|2015/01/14||1410–1640||Trough + high |
|2015/01/25||1000–1230||Trough + high |
|2015/11/05||1210–1630||Ridge + high |
|2015/11/11||1308–1620||Weak trough + |
|2015/11/17||1730–2020||Weak trough + |
|2016/01/16||0958–1145||Trough + easterly |
|2016/01/16||1820–2145||Trough + easterly |
|2016/01/21||0730–0935||Weak trough + |
|2016/11/29||1205–1545||Trough + high pressure||9841||–35.2– –5.7|
|2016/12/12||1245–1620||Trough + high |
|2017/02/07||0939–1156||Trough + high |
|2017/11/24||1541–1818||Trough + low |
|2020/01/05||1528–1718||Ridge + high |
|2020/12/28||0954–1300||Weak trough + |
|2021/01/14||0938–1240||Trough + low |
|2021/01/14||1600–1850||Trough + low |
|2021/01/25||1038–1142||Trough + high |
|2021/01/27||1538–1725||Trough + low |
|2021/02/23||0958–1225||Trough + high |
|2021/02/27||1614–1840||Ridge + high |
|2021/02/28||1041–1228||Ridge + easterly |