Abstract
Abstract. Halo displays in the sky contain valuable information about ice crystal shape and orientation: e.g., the 22° halo is produced by randomly oriented hexagonal prisms while parhelia (sundogs) indicate oriented plates. HaloCam, a novel sun-tracking camera system for the automated observation of halo displays is presented. An initial visual evaluation of the frequency of halo displays for the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign from October to mid-November 2014 showed that sundogs were observed more often than 22° halos. Thus, the majority of halo displays was produced by oriented ice crystals. During the campaign about 27 % of the cirrus clouds produced 22° halos, sundogs or upper tangent arcs. To evaluate the HaloCam observations collected from regular measurements in Munich between January 2014 and June 2016, an automated detection algorithm for 22° halos was developed, which can be extended to other halo types as well. This algorithm detected 22° halos about 2 % of the time for this dataset. The frequency of cirrus clouds during this time period was estimated by co-located ceilometer measurements using temperature thresholds of the cloud base. About 25 % of the detected cirrus clouds occurred together with a 22° halo, which implies that these clouds contained a certain fraction of smooth, hexagonal ice crystals. HaloCam observations complemented by radiative transfer simulations and measurements of aerosol and cirrus cloud optical thickness (AOT and COT) provide a possibility to retrieve more detailed information about ice crystal roughness. This paper demonstrates the feasibility of a completely automated method to collect and evaluate a long-term database of halo observations and shows the potential to characterize ice crystal properties.
Reference59 articles.
1. Alpaydin, E.: Introduction to Machine Learning, Adaptive Computation and Machine Learning, 2nd edn., MIT Press, Cambridge, 2010.
2. Baran, A. J. and Labonnote, L. C.: On the reflection and polarisation properties of ice cloud , J. Quant. Spectrosc. Ra., 100, 41–54, https://doi.org/10.1016/j.jqsrt.2005.11.062, 2006.
3. Baran, A. J., Furtado, K., Labonnote, L.-C., Havemann, S., Thelen, J.-C., and Marenco, F.: On the relationship between the scattering phase function of cirrus and the atmospheric state, Atmos. Chem. Phys., 15, 1105–1127, https://doi.org/10.5194/acp-15-1105-2015, 2015.
4. Bradski, D. G. R. and Kaehler, A.: Learning Opencv, 1st edn., O'Reilly Media, Inc., Sebastopol, 2008.
5. Breiman, L.: Bagging Predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1023/A:1018054314350, 1996.
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献