Abstract
The ice crystal habits, distributions and growth processes in two snowfall cloud cases on 29 November 2009 and 3 March 2012 in northern China were compared and analyzed with aircraft data. The results showed that ice crystal habits were affected by the height of ice clouds. Ice crystals in clouds with cloud top temperatures of −12.6 °C were predominantly needle, plate, dendrite and irregular. When the cloud top temperature was lower than −19.5 °C, plates, dendrites and irregular ice crystals were observed in addition to needles, capped-column crystals were observed in the lower and middle layers of clouds, and column crystals were observed in the upper layer of clouds. The liquid water content of the two snowfall processes was lower than 0.1 g·m−3. Ice particles grew mainly via deposition, riming and aggregation processes. On 29 November, the liquid water content of the stratospheric mixed snowfall cloud was distributed in the lower part of the cloud. The maximum values of particle concentration and ice water content detected by a cloud imaging probe were 187 L−1 and 1.05 g·m−3, which were at −8.7 °C, and the ice water content was higher. On 3 March, the liquid water content of snowfall in stratiform clouds was located in the middle layer, and the maximum ice water was low, which was only 0.052 g m−3. The ice water value on 29 November was higher, which was mainly due to the convective zone embedded in the cumulus mixed cloud containing a large number of riming and aggregated snow crystals. Using an exponential function to fit the crystal spectrum of the two snowfall processes, N0 and λ were 109−1011 m−4 and 108−1010 m−4 and 103−104 m−1 and 104 m−1, respectively. Compared with 3 March, N0 on 29 November was larger and the variation range of λ was one more order of magnitude. N0 and λ conformed to a power function distribution. By analyzing the scatter plot of the correlation coefficient and slope, it was found that the exponential function can accurately express the crystal spectrum of snow clouds.
Highlights
It is very important to research the size, shape and concentration of ice crystals to better understand the physical process of snow cloud formation and the radiation effects of global climate change [1]
Field and Heymsfield [21] proved that the variation in fitting parameters with temperature in the exponential form of the snow crystal spectrum may be caused by many microphysical processes, such as sublimation, deposition and collision of ice particles
The peak area of ice and snow crystal number concentration was observed in the upper part of the snowfall cloud, which was dominated by deposition growth, and the peak area of ice and snow crystal number concentration was formed in the middle part of the cloud, which was dominated by aggregation and riming growth
Summary
It is very important to research the size, shape and concentration of ice crystals to better understand the physical process of snow cloud formation and the radiation effects of global climate change [1]. Field and Heymsfield [21] proved that the variation in fitting parameters with temperature in the exponential form of the snow crystal spectrum may be caused by many microphysical processes, such as sublimation, deposition and collision of ice particles. The vertical distribution characteristics of ice and snow crystals in different regions of different weather conditions are analyzed by combining the cloud particles detected by aircraft with radar data along the flight path. We analyzed the vertical distribution of ice and snow crystal morphology with temperature, the microphysical process of the formation and growth of ice and snow crystal morphology, and the distribution and corresponding spectral type parameters of ice and snow crystals under different weather conditions and combined these findings with the detection data, including particle spectra and liquid water and ice water content data and images. The results provide a scientific basis for further understanding the snow formation mechanism and improving the parameterization process of clouds in models
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