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--> --> -->Many in-depth studies have been conducted regarding icing in terms of the temporal and spatial distribution of freezing weather disasters, circulation conditions, weather systems, physical mechanisms, monitoring, and early-warning methods. (Wang, 2011) analyzed the temporal and spatial distributions of ice-freezing days in China from 1954 to 2009 and pointed out that China's heavy icing areas were mainly in northern Xinjiang, southern Shaanxi, the central part of northeastern China, the eastern part of northern China, Qinling, northeastern Yunnan, Guizhou, and other places, primarily from November to late March. (Li et al., 2015) used the observational data of China's civil aviation airports from 2011 to 2013 to analyze temporal and spatial distributions and the weather conditions of freezing rain, freezing drizzle, and freezing fog. They pointed out that freezing rain, freezing drizzle, and freezing fog appeared in January with frequencies as high as 55%, 67%, and 38%, respectively. Other studies (Ding et al., 2008; Wang et al., 2008a, 2008b; Yang et al., 2008) have pointed out that a large area of freezing weather is usually accompanied by a unique circulation pattern. The center of the polar vortex located in the northern part of the eastern hemisphere, a quasi-steady blocking condition at mid and high latitudes, and an active southern branch of circulation in the low-latitude area were the main synoptic causes for freezing rain and snow disasters in southern China at the beginning of 2008. (Zeng et al., 2008), (Li et al., 2009), and (Tao et al., 2012) studied the stratification characteristics of freezing rain and found that a stable, thicker melting layer was the direct cause for freezing rain encompassing a wide area. The main mechanisms for freezing rain formation were ice-crystal melting and supercooled warm-rain processes (Tao et al., 2012). (Zhou et al., 2012) and (Liu and Niu, 2016) studied real-time icing observational data of 500-kV high-voltage transmission lines in the Central China Power Grid in Hubei Province in the winters of 2008 and 2009. Both temperatures for icing formation and shedding were lower than the temperature thresholds on the test cable. Icing formed easily when the temperature was about -2°C, relative humidity was greater than 95%, and wind speed was 0-1 m s-1. (Jiang, 1984) established the relationship between the growth rate of icing and meteorological elements by using icing accretion observation data from the Lushan Cloud Test Station in China in 1978-81. (Tan, 1982) found that the ratio of ice thickness at two different heights was a power function of the ratio of the two heights. (Jones, 1998) proposed a model for freezing rain ice load, with parameters such as precipitation rate and wind speed. (Makkonen, 1989) proposed a wet snow accretion model by using the wet-bulb temperature as the index to judge the occurrence of wet snow, considering visibility as an input. Other studies have applied and improved this model (Makkonen and Wichura, 2010; Nygaard et al., 2013). (Drage and Hauge, 2008) combined the MM5 model with an ice accretion model to simulate ice accretion on the west coast of Norway. (Musilek et al., 2009), (Pytlak et al., 2010), and (Hosek et al., 2011) combined the WRF model with an ice accretion model to develop an ice accretion forecasting system.
(Luo et al., 2008) studied the microphysical characteristics of cloud and fog during an icing period by analyzing the cloud droplet spectrum and conventional meteorological data in a freezing area of Guizhou Province, China. They found that cloud droplets greater than 14 μm were an important factor for icing growth. (Jia et al., 2010) studied a mixed icing process on the basis of glaze conditions at Enshi Radar Station in China from 25 February to 4 March 2009 and analyzed the features of fog droplet and precipitation particle spectra under three kinds of weather. Their results showed that the average concentration of fog droplets was 224 cm-3 on rainy days, 181 cm-3 on rainy and snowy days, and 139 cm-3 on foggy days. (Niu et al., 2012) studied the microphysical characteristics of fog droplet and raindrop spectra during the formation of icing by using the observation data of icing at Enshi Radar Station in Hubei Province of China in the winters of 2008/09 and 2009/10. They pointed out that there was a positive correlation (0.62) between liquid water content in the rain/fog and icing growth rate. (Zhou et al., 2013) studied the effect of freezing drizzle on icing growth during fog processes and pointed out that the direct contribution of freezing drizzle to ice thickness was 14.5%. The inclusion of microphysical processes in these studies helps us further understand wire icing accretion, as opposed to the initial studies that only considered meteorological conditions.
We conducted meteorological condition and microphysical observations of icing accretion from December 2015 to February 2016 at the Lushan Meteorological Bureau Observatory, Southeast China. To study the effect of different heights on icing, we included icing gradient observations. This study is a comprehensive analysis of the distribution of the supercooled fog droplet spectrum and the mechanism of icing growth in the icing process from 20-25 January 2016.
The data measured by instruments and used in this study are listed in Table 1. The sensor head of the TPS-3100 hotplate total precipitation sensor has double discs, which provide precipitation rate measurements of rain and snow as part of the automatic weather observation. Its detection range is 0-50 mm h-1 and the temporal resolution is 1 min. The OWI-430 weather identifier and visibility sensor measures precipitation by detecting the optical irregularity induced by particles falling through a beam of partially coherent infrared light, and it calculates visibility by determining the amount of forward scattered light by particles. Its detection range for visibility is 0.001～10 km. The FM-100 fog droplet spectrometer has been widely used in ice and freezing fog observations (Gultepe et al., 2011, 2016).. There is a 10-m tower on the west side of the observatory, and wires were laid on the 10-m tower and on the 1.5-m icing frames under the tower. Icing growth was observed. There were sets of wires in the east-west, north-south, and northeast-southwest directions on the tower. Limited by observation and severe weather conditions, only the ice thickness data of the northeast-southwest wire were used to study the icing accretion on the tower, representing the average icing conditions of the wires (along east-west and north-south). The icing frame under the tower was laid in accordance with "ground meteorological observation norms": one group was oriented along the east-west direction, and the other group was oriented along the north-south direction. The wire diameter is 26.8 mm. Ice diameter a (the maximum of accumulated ice on the cut surface perpendicular to the wire, the wire diameter is included) and ice thickness b (the maximum of the accumulated ice perpendicular to the ice diameter on the cut surface of the wire) were measured hourly (China Meteorological Administration, 1979).
Through the observation during a cold surge, a more complete and higher temporal resolution fog droplet spectrum, visibility, weather phenomena, and conventional meteorological elements could be obtained. For this study, we also used MICAPS and NCEP reanalysis data.
The wire diameter is φ, the equivalent ice diameter is D, the equivalent ice thickness is W, and the calculation formulas are as follows: \begin{eqnarray} D=\sqrt{ab} ;\ \ (1)\\ W=\frac{D-\phi}{2} . \ \ (2)\end{eqnarray} The ice thickness mentioned later is the equivalent ice thickness. During an icing process, the ice on blade type anemometer was removed from time to time, which resulted in zero wind speed. Such (zero) data was removed during data processing. Sometimes, non-zero wind data might be collected when ice was not completely removed, which means that the observed wind speed data could be less than what the actual wind speed was. However, this is inevitable in freezing weather, and wind speed data still have a reference value.
The atmospheric stratification at the observation station during the icing process was obtained from the NCEP reanalysis data (not shown). The temperature at 850 hPa was 0°C from 0800 LST 19 to 0800 LST 20 January, before the cold front crossing. Relative humidity was below 90%, and the wind direction was mainly southeasterly and southwesterly from 900 to 500 hPa. When the cold front crossed from 20-23 January, the low-level system was primarily composed of the northeasterly wind, and the wind speed increased. The high-level system was primarily composed of the southwesterly wind. Temperatures dropped below 0°C, and relative humidity increased to greater than 90% from 900 to 500 hPa. There was no warm layer above the observation site during the rain and snow process (from 0815 LST 20 to 1000 LST 23 January), as the stratification exhibited "a single layer structure" (Li et al., 2009). Primarily, a northwesterly wind resided from 900 to 500 hPa after 0000 LST 24 January. The transport of cold air further reduced the temperature, but the weather turned out to be fine and the relative humidity dropped below 50%. Cooling and humidification were conducive to icing growth and maintenance.
Rain and snow were sustained for a long time at the Lushan Observatory during the cold surge. Rainfall and snowfall durations were 14 h and 60 h, respectively, and the entire process resulted in a 15°C cooling. As shown in Fig. 1a, rain began at 0815 LST 20 January, and the precipitation rate (rain and snow were both measured using the hotplate total precipitation sensor, collectively referred to as the precipitation rate) began to decrease after 1800 LST 20 January. It started to snow at 2200 LST 20 January, and it snowed intermittently from 21-23 January, stopping at 1000 LST 23 January. The maximum precipitation rate was 6.1 mm h-1 (at 1357 LST 20 January) during the entire process, with an average of 0.2 mm h-1. The surface snow depth (Fig. 1b) continued to rise from 1000 LST 21 January, reached a maximum of 15 cm at 0500 LST 23 January, and then continued to decline after the snow stopped, until the snow melted on 25 January.
Figure1. Temporal variations (LST) of (a) precipitation rate, (b) snow depth, (c) visibility, (d) temperature at the 1.5-m height T1.5, (e) fog droplet number concentration N, (f) fog droplet mean diameter D ave, and (g) fog liquid water content (LWC).Rain and snow occurred along with the emergence of fog. Visibility (Fig. 1c) continuously declined from 5.9 km at 0800 LST 20 January. It was below 1 km at 1255 LST 20 January, which indicated the beginning of fog. The fog process ended when visibility rose above 1 km at 1039 LST 23 January. The temperature at the 1.5-m height, T1.5 (Fig. 1d), declined below zero at 1636 LST 20 January; afterwards, the fog became supercooled. Fog appeared intermittently, during which the fog droplet number concentration (N; Fig. 1e), mean fog droplet diameter (D ave; Fig. 1f), and fog liquid water content (LWC; Fig. 1g) all fluctuated significantly, with mean values of 52 cm-3, 4.1 μm, and 0.01 g m-3, respectively. LWC was less than previous observed results of mountain fog (Wu et al., 2007) and wire icing microphysics (Luo et al., 2008; Jia et al., 2010; Niu et al., 2012). This might be due to the discontinuity of this fog process and to scouring of raindrops and snow particles on fog droplets, among others. According to the criterion of N larger than 10 cm-3 and LWC larger than 0.001 g m-3 (Lu et al., 2013), 218 agglomerate fogs were identified in this fog process. Figure 2a shows the frequency distribution of the durations of agglomerate fogs. Agglomerate fogs with durations less than 5 min had the highest appearance frequency (78.4%). Agglomerate fogs with durations less than and greater than 10 min accounted for frequencies of 91.3% and 8.7%, respectively. Consistent with the statistical results of (Hodges and Pu, 2016), the frequency of agglomerate fogs decreased rapidly with increasing duration. As the correlations of agglomerate fog duration with N, D ave, and LWC were low, we speculate that the duration of agglomerate fog was controlled by the weather system. Figure 2b shows the frequency distribution of time intervals between agglomerate fogs, with a frequency of 88.0% for a period of 0 to 10 min, a maximum time interval of 436.2 min, and an average time interval of 10.3 min. The fog from 1740-1911 LST 22 January (90.6 min) had the longest duration; its average N, D ave, LWC, and peak diameter were 131 cm-3, 5.2 μm, 0.02 g m-3, and 4.7 μm, respectively. Figure 2c shows that the average droplet spectrum of the longest fog was a bimodal distribution with two peak diameters at 4.9 μm and 8.9 μm, which satisfied the K-M distribution with a goodness of fit of R2=0.92. These agglomerate fog durations, frequency, time intervals, and other statistical results provide the basic data for developing landscape meteorology.
Figure2. (a) Frequency distribution of fog durations, (b) frequency distribution of fog time intervals, and (c) fitting of K-M distribution of fog during 1740-1911 LST 22 January.Rain and snow appeared at the same time with fog and impacted the fog droplet spectrum to some degree. However, current research on the microphysical characteristics of rain fog and snow fog is limited. Figure 3 shows fog droplet spectra under different precipitation rates. At the time of rainfall (Fig. 3a), the number concentration increased with an increase in precipitation rate for 2-15 μm and 30-50 μm particles. When rain was relatively heavy, evaporation increased relative humidity, which was beneficial to the growth of fog droplets. At the same time, the collision and break of raindrops also caused the number concentration to rise. When the precipitation rate was 3-4 mm h-1, the spectral pattern of fog droplets significantly changed. Figure 3b shows that the number concentration decreased with an increase in precipitation rate for 2-27 μm particles during snowfall. When ice crystals and fog droplets coexisted, moisture spread from water droplets to ice crystals (ice crystal effect) (Yang et al., 2011; Zhou et al., 2016), which was not conducive to the growth of fog droplets. At the same time, the number concentration of fog droplets decreased due to the coagulation of ice crystals. The decrease in number concentration of large fog droplets in 27-50 μm particles was not significant; this was because the ice density was smaller——the ice floated when it was close to the large fog droplets and moved with the flow field around water droplets. As a result, collision efficiency was reduced (Yang et al., 2011). When the precipitation rate was 2-3 mm h-1, the spectral pattern of fog droplets was significantly changed.
Figure3. Average fog droplet spectra under different precipitation rates: (a) rain; (b) snow.Figure 4 shows the temporal evolution of elements such as ice thickness at the 10-m height. According to the variation in ice thickness, an icing process can be divided into the icing preparation period, growth period, maintenance period, and fall-off period. Table 2 shows the ice thickness and meteorological elements for the four periods at the 10-m height.
Figure 4b shows that temperature at the 10-m height, T10, was slightly higher than zero (average 0.5°C) during the icing preparation period. With the occurrence of precipitation, relative humidity increased from 20% to above 90%, and the average wind speed was 0.7 m s-1. Icing began to appear at 1700 LST 20 January. The T10 dropped to below zero and was -1.8°C at the beginning of icing formation. The average temperature in the growth period was -6.3°C, while relative humidity was steady in the 81.7%-98.7% range. The wind speed was significantly greater than that in the icing preparation period, with a maximum value of 9.1 m s-1, while ice thickness increased to 17.9 mm. According to the observation, snow stopped and fog dissipated at about 1100 LST 23 January, as the maintenance period began. Solar radiation increased from 7.0 W m-2 at 0806 LST 23 January to 695.0 W m-2 at 1217 LST 23 January, which prompted the increase in T10 from -10.6°C to -5.9°C, followed by a gradual decrease to the lowest value of -15.5°C (0756 LST 24 January). Changing with T10 in sync, relative humidity dropped from 98.0% to 67.3% and then rose to 89.7%. Compared to the wind speed in the growth period, the wind speed in the maintenance period decreased somewhat, with an average speed of 2.3 m s-1. Ice thickness was steady and fluctuated slightly, with an average of 18.0 mm. In the fall-off period, solar radiation peaked at 615.0 W m-2 (1157 LST) on 24 January; in the afternoon, relative humidity reached a minimum of 45.7% (1422 LST), and T10 had a peak of -10.1°C (1433 LST). This increased the wire surface temperature, causing icing to diminish. The average wind speed in this period was 3.3 m s-1, which was larger than that in the maintenance period, accelerating the icing fall-off (Zhou et al., 2012).
Figure4. Temporal variations (LST) of (a) ice thickness (black) and icing growth rate (red), (b) Temperature T10 (black) and relative humidity (red), (c) wind velocity, (d) solar radiation, (e) precipitation rate, (f) fog droplet number concentration N, (g) fog liquid water content (LWC), and (h) fog droplet mean diameter D ave, in the period of wire icing at the 10-m height.The growth period of icing on the tower was sustained for 65.7 h. To eliminate the influence of factors such as measurement errors on the calculation of the icing growth rate, the five-point moving average of ice thickness was used to calculate the hourly icing growth rate, as shown in Fig. 4a. According to the precipitation type and the growth of icing, the icing growth period was divided into the rain phase, snow phase 1, and snow phase 2 (Fig. 4a). Average ice thickness, growth rate of ice, T10, relative humidity, wind velocity, precipitation rate, and the microphysics of fog droplets in each phase were calculated (Table 3). The fog droplet spectral evolution was also analyzed (see Fig. 5). The three phases were compared in terms of meteorological conditions and fog droplet microphysical characteristics.
The average T10 during the rain phase was -4.3°C, the precipitation type was freezing rain, and the relative humidity (97.5%) was the highest for the three phases. In addition, supercooled fog appeared. The fog droplet spectrum had a single peak distribution in the rain phase (1700-2200 LST 20 January; Fig. 5a), with a peak at 2.8 μm. This phase was sustained for 5 h, during which time fog appeared (visibility less than 1 km) for 3.5 h, accounting for 70.0% of the total length of time; this was the largest ratio for the three phases. Compared with fog droplets, the collection efficiency of wire for raindrops was higher (Lamraoui et al., 2014), so the icing growth rate (1.3 mm h-1) was the largest in this phase, compared to the latter two phases without freezing rain.
The precipitation type in snow phase 1 was mainly snow. The temperature described in previous studies of the wet snow icing process was roughly 0°C (Makkonen, 1989; Makkonen and Wichura, 2010), but the average temperature in this phase was -5.7°C on the 10-m tower. Moreover, the stratification analysis in section 3 shows that the entire atmosphere above the observation site was below 0°C and without a melting layer during snowfall. Therefore, the snowfall in this phase was dry snow. The precipitation rate (0.4 mm h-1) was the highest for the three phases, but the surface of the snow particles did not melt, so the sticking efficiency (ratio of the flux density of the particles that stick to the object to the flux density of the particles that hit the object) of wire was lower (Makkonen, 2000). The wind speed (2.5 m s-1) and occurrence frequency of fog (16.9%) in snow phase 1 were also the lowest for the three phases. Figures 4f, g, and h show that N, LWC, and D ave were less than 1 cm-3, 0.01 g m-3, and 3 μm, respectively, at 0800-1200 LST 21 January and 1500-1700 LST 21 January, which resulted in the lowest average LWC (0.006 g m-3), N (31 cm-3), and D ave (3.4 μm) for the three phases, failing to provide rich moisture for the growth of icing. Figure 5 shows the evolution of the fog droplet spectrum in snow phase 1. Compared with the rain phase, the number concentration increased in every spectral range during 2200 LST 20 to 0500 LST 21 January (Fig. 5b). However, the average icing growth rate decreased to 0.2 mm h-1 because freezing rain switched to dry snow. During 0500-1400 LST 21 January (Fig. 5c), the number concentration was the lowest for all stages in every spectral range except for 33.5-50.0 μm, and the average icing growth rate decreased to -0.2 mm h-1. During 1400 LST 21 to 0800 LST 22 January (Fig. 5d), the number concentration increased again in every spectral range, and the average icing growth rate rose to 0.1 mm h-1. During 0800-1200 LST 22 January (Fig. 5e), the number concentration decreased for particles smaller than 27.5 μm and increased for particles larger than 27.5 μm; the average icing growth rate was still 0.1 mm h-1 in this stage. The average icing growth rate was 0.1 mm h-1 in snow phase 1, icing grew slowly, and the ice thickness remained around 6.3 mm.
Figure5. Average fog spectra in each stage of the icing process: (a) 1700-2200 LST 20 January; (b) 2200 LST 20 to 0500 LST 21 January; (c) 0500-1400 LST 21 January; (d) 1400 LST 21 to 0800 LST 22 January; (e) 0800-1200 LST 22 January; and (f) 1200 LST 22 to 1041 LST 23 January.The precipitation type in snow phase 2 was also dry snow. Its precipitation rate (0.2 mm h-1) was lower than that in snow phase 1, but its icing growth rate (0.5 mm h-1) was higher than that in snow phase 1. In this phase, icing grew fast again. Because the adhesion effect of dry snow particles was weak, a higher precipitation rate in snow phase 1 could not effectively accelerate icing growth. The wind speed rapidly increased to 9.1 m s-1 after 1200 LST 22 January in snow phase 2 (Fig. 4c). The average wind speed in snow phase 2 was the highest (4.2 m s-1) for the three phases, which raised the collision efficiency (the ratio of the flux density of the particles that hit the object to the maximum flux density) of fog droplets and snow particles to the wire (Makkonen, 2000; Davis et al., 2014). The T10 (-7.8°C) in snow phase 2 was the lowest for the three phases, which was favorable for the freezing of fog droplets (Lamraoui et al., 2014). The occurrence frequency of fog in snow phase 2 (45.8%) was higher than that in snow phase 1; the LWC (0.012 g m-3), N (83 cm-3), and D ave (5.1 μm) in snow phase 2 were all the highest for the three phases. Figure 5f also shows that the number concentration of the fog droplet spectrum increased in all spectral ranges in snow phase 2 (1200 LST 22 to 1041 LST 23 January) compared with the previous stage (0800-1200 LST 22 January), which provided a large number of supercooled liquid droplets for icing growth.
Icing grew rapidly in the freezing rain phase but slowly in the dry snow phase. The intensive occurrence of supercooled fog, lower temperatures, and increased wind speed accelerated icing growth in the dry snow process.
5.1. Icing at the 1.5-m height
Figure 6 shows the evolution of ice thickness, temperature (T1.5), relative humidity, wind speed, and solar radiation at the 1.5-m height. Icing appeared from 2300 LST 20 January until it fell off the wire at 1200 LST 23 January. Average ice thickness on the east-west and north-south wires were 0.5 and 0.9 mm, respectively, which differed by 44.4%. To study the effect of wind direction on icing growth, the wind direction during icing was statistically analyzed. Wind direction was mainly northeasterly during the icing process. The frequency of the wind direction (with respect to the north-south direction) at greater than 45° was 52.9%, and that at less than 45° was 47.1%. When the wind direction and the wire were perpendicular, the yield was largest (Luo et al., 2008). Because the frequency of wind direction (with respect to the north-south direction) at greater than 45° was higher, the ice thickness on the north-south wire was thicker than that on the east-west wire.
Figure6. Temporal variations (LST) of (a) ice thicknesses (black) and difference of ice thicknesses between two directions ? W1 (red), (b) temperature T1.5, (c) relative humidity, (d) wind velocity, and (e) solar radiation in the period of wire icing at the 1.5-m height.Ice thickness at the 1.5-m height showed significant diurnal variation. Ice thickness increased at night (2000-0800 LST) and decreased during the day (0800-2000 LST). Ice thickness showed minima (0.4 and 0.3 mm on the east-west wire; 0.7 and 0.6 mm on the north-south wire) around 1600 LST 21 January and 1400 LST 22 January. Variation in ice thickness is generally sensitive to variations in temperature (Zhou et al., 2012) and relative humidity during the day when ice thickness is relatively thinner. At 1203 LST 21 January and 1221 LST 22 January, solar radiation had significant peaks of 114.0 and 346.0 W m-2, respectively; T1.5 had peaks of -2.3°C and -2.5°C, respectively; relative humidity minima were 83.6% and 81.7%, respectively; while the ice thickness reached its minima at these times.
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5.2. Comparisons of icing growth and meteorological elements at two different heights
Figure 7 and Table 4 compare the ice thickness and meteorological elements at the two heights. The differences in icing at the two heights were mainly manifested in four aspects. First, the ice thickness revealed a wide difference, with maximum ice thickness at 10 and 1.5 m of 20.7 and 1.2 mm, respectively; the latter was 5.8% of the former. Second, the durations of icing were quite different. The durations were 102 and 61 h, respectively; icing at 1.5 m only appeared in the growth period of 10-m icing. Third, the icing density revealed wide differences; there was translucent solid ice with larger density at 1.5 m and white sponge-like ice with lower density at 10 m. Fourth, there was a difference in the icing growth mechanism. The accumulation rate of icing at 10 m was closely related to factors such as the precipitation rate and microphysical characteristics of supercooled fog. The ice thickness at 1.5 m was sensitive to daily variations in temperature and relative humidity, indicating diurnal variation.
Figure7. Temporal variations (LST) of (a) ice thickness at 10-m height and difference of ice thicknesses between two heights ? W2, (b) ice thickness at 1.5-m height, (c) temperature at 10-m height T10, temperature at 1.5-m height T1.5, grass temperature T s, ground temperature T d, 5-cm ground temperature T d5, 10-cm ground temperature T d10, 15-cm ground temperature T d15, and 20-cm ground temperature T d20, (d) wind velocities at two different heights, and (e) wind velocity difference at two different heights ? V and temperature difference between the two heights ? T in the period of wire icing.The reasons for these differences are analyzed next. On the one hand, the differences in ice thickness and durations were due to differences in temperature. Figure 7c shows the temporal variations of air and ground temperatures of each height from 0000 LST 20 to 0000 LST 25 January. In general, the range of ground temperatures was less than that of air temperatures. The temperature decreased with height during icing. Caused by the height difference, the temperature on the tower of T10 was less than that under the tower of T1.5. In particular, the maximum temperature difference appeared during the icing growth period (Fig. 7e), and the average temperatures differed by 1.1°C. The lower temperature on the tower was favorable for freezing on a wire of fog droplets and raindrops (Lamraoui et al., 2014). On the other hand, average wind speeds were 3.1 and 1.6 m s-1 on and under the tower, respectively, in the icing growth period. The wind speed on the tower was much larger than that under the tower, especially from 1200 LST 22 to 1000 LST 23 January (Fig. 7e), during which icing on the tower accumulated rapidly to 17.9 mm. Wind speed was an important factor in icing growth (Nygaard et al., 2013), which raised the collision efficiency of fog droplets and snow particles on the wire (Davis et al., 2014). The difference in density was caused by the difference in temperature; ice density was higher when the temperature was higher, and it was lower when the temperature was lower (Luo et al., 2008). The difference in the icing growth mechanism was also due to differences in temperature and wind speed. The environment of low temperature and strong wind caused fog droplets and snow particles to rapidly freeze on the wire. However, it was difficult for fog droplets and snow particles to freeze on the wire under the tower, so the ice thickness at 1.5 m was thinner and more vulnerable to ambient temperature and humidity. Environmental conditions were different at different heights during the same icing process, resulting in large differences in factors such as thickness, duration, density, and growth mechanism.
A cold surge was the background of this icing process. The entire atmospheric layer was below 0°C during the icing process and there was no warm layer. Continuous reduction in temperature during this process turned rain into snow, and fog appeared intermittently. Freezing rain, snow, and supercooled fog provided rich moisture sources for icing growth.
There were 218 agglomerate fogs, of which durations of less than and greater than 10 min accounted for 91.3% and 8.7%, respectively, and the average time interval was 10.3 min. The duration of agglomerate fog, time intervals, and other statistical features provided basic data for developing landscape meteorology. At the time of rainfall, the number concentration increased with an increase in precipitation rate for 2-15 μm and 30-50 μm particles, and the number concentration decreased with an increase in the precipitation rate for 2-27 μm particles during snowfall.
The icing observation at 10 m on the tower was more like the situation of actual high-voltage transmission lines. The icing growth period at 10 m was divided into three phases: rain phase, snow phase 1, and snow phase 2. The main precipitation type in the rain phase was freezing rain, and the icing growth rate (1.3 mm h-1) in the rain phase was the largest for the three phases. The precipitation type in snow phase 1 was dry snow, when icing grew slowly with a growth rate of 0.1 mm h-1. The precipitation type in snow phase 2 was also dry snow, with intensive occurrence of supercooled fogs, lower temperatures (-7.8°C on average), and increased wind speed (4.2 m s-1 on average), which accelerated the icing growth rate (0.5 mm h-1) in this dry snow process.
The maximum ice thickness at 10 and 1.5 m was 20.7 and 1.2 mm, respectively, while the duration was 102 and 61 h, respectively. The density of icing at 10 m was lower than that at 1.5 m, and the accumulation rate of icing on the tower was closely related to the precipitation rate and microphysical characteristics of supercooled fogs. The ice thickness at 1.5 m was sensitive to daily variations in temperature and relative humidity, indicating diurnal variation. In the icing growth period, the mean temperatures at the 10-m and 1.5-m heights were -6.3°C and -5.2°C, respectively, and the mean wind speeds were 3.1 and 1.6 m s-1, respectively. The differences in temperature and wind speed were the main reasons for the differences in thickness, duration, density, and growth mechanism of icing at the two heights, which indicated that the icing thickness, duration, density, and growth mechanism were all closely related to height. We suggest that icing gradient observations be conducted, the height of icing observation frames should be raised, more comparative observation studies of the relationship between different heights and icing growth should be conducted to make observation results closer to the icing on actual high-voltage transmission lines, and new and more realistic icing observation methods should be established.
This work mainly analyzed observed data and discussed the ice accumulation process with as much detail as possible. Further observational results show that the sticking efficiency of snow particles has a significant impact on icing growth rate, which will be considered in the next wire-icing numerical simulation. We will be considering collaborations in simulation research, so experts who are interested in the observed data of this icing process may contact the corresponding author to discuss further research.
