Observation and Simulation Studies of Three Types of Wire Icing
Tianshu Wang, Shengjie Niu, Jingjing Lv, Yue Zhou, Yuan Wang
Abstract
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Tianshu Wang, Shengjie Niu, Jingjing Lv, Yue Zhou, Yuan Wang
Abstract
Open-access reader
Research on three types of wire icing evolution mechanism is of great significance for ice disaster recognition and prediction. Comprehensive field observations of wire icing were conducted in the winters of 2015/2016 and 2016/2017 at the Lushan Mountain Meteorological Bureau Observatory (elevation 1164.5 m), Jiangxi Province, China, and the ice thickness, weather phenomena, meteorological elements, and fog spectrum were measured. Four icing cases were recorded, in which high ice-producing conditions, such as freezing rain, snow and supercooled fog, occurred. The icing growth and shedding mechanisms in three types of weather (freezing rain, snow, and supercooled fog) were analyzed and compared. Considering mixed ice-producing conditions, the ice thickness was simulated by integrating freezing rain, snow, and supercooled fog icing models, with inputs such as the precipitation rate and wind speed. The average measured icing growth rates in freezing rain, snow, and supercooled fog were 0.4, 0.3, and 0.2 mm h−1, respectively. The correlations between the icing growth rate and both the temperature and the wind speed were stronger in the snow and supercooled fog than in freezing rain. With the decreasing temperature, the icing growth rate increased faster with snow, while that in supercooled fog increased faster as the wind speed increased. In freezing rain, snow, and supercooled fog, the icing growth rates were all positively correlated with the ice thickness, with correlation coefficients of 0.55, 0.67, and 0.79, respectively. Ice was shed when the temperature remained below 0 °C, the wind speed fell to 2.7 m s−1, and the fog liquid water content fell to 0.036 g m−3 in supercooled fog, and when the solar radiation increased and the temperature exceeded 0 °C in freezing rain. The average sticking efficiency of the wire to snow was 0.03; its variation range was 0.01–0.10. The integrated model can simulate the changes in actual ice thickness well.
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Research on three types of wire icing evolution mechanism is of great significance for ice disaster recognition and prediction. Comprehensive field observations of wire icing were conducted in the winters of 2015/2016 and 2016/2017 at the Lushan Mountain Meteorological Bureau Observatory (elevation 1164.5 m), Jiangxi Province, China, and the ice thickness, weather phenomena, meteorological elements, and fog spectrum were measured. Four icing cases were recorded, in which high ice-producing conditions, such as freezing rain, snow and supercooled fog, occurred. The icing growth and shedding mechanisms in three types of weather (freezing rain, snow, and supercooled fog) were analyzed and compared. Considering mixed ice-producing conditions, the ice thickness was simulated by integrating freezing rain, snow, and supercooled fog icing models, with inputs such as the precipitation rate and wind speed. The average measured icing growth rates in freezing rain, snow, and supercooled fog were 0.4, 0.3, and 0.2 mm h−1, respectively. The correlations between the icing growth rate and both the temperature and the wind speed were stronger in the snow and supercooled fog than in freezing rain. With the decreasing temperature, the icing growth rate increased faster with snow, while that in supercooled fog increased faster as the wind speed increased. In freezing rain, snow, and supercooled fog, the icing growth rates were all positively correlated with the ice thickness, with correlation coefficients of 0.55, 0.67, and 0.79, respectively. Ice was shed when the temperature remained below 0 °C, the wind speed fell to 2.7 m s−1, and the fog liquid water content fell to 0.036 g m−3 in supercooled fog, and when the solar radiation increased and the temperature exceeded 0 °C in freezing rain. The average sticking efficiency of the wire to snow was 0.03; its variation range was 0.01–0.10. The integrated model can simulate the changes in actual ice thickness well.
Key concepts: Icing, Snow, Freezing rain, Supercooling, Environmental science, Icing conditions, Atmospheric sciences, Rain and snow mixed