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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2018-401
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2018-401
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Jan 2019

Research article | 07 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Analysis Algorithm for Sky Type and Ice Halo Recognition in All-Sky Images

Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit Sylke Boyd et al.
  • Division for Science and Mathematics, University of Minnesota-Morris, 500 E 4th Street, Morris, MN

Abstract. Ice halos, in particular the 22° halo, have been captured in long-time series of images obtained from Total Sky Imagers (TSI) at various Atmospheric Radiation Measurement (ARM) sites. Ice halos form if smooth-faced hexagonal ice crystals are present in the optical path. We describe an image analysis algorithm for long-time series of TSI images which identifies images with 22° halos. Each image is assigned an ice halo score (IHS) for 22° halos, as well as a sky type score (STS), which differentiates cirrostratus (CS), partially cloudy (PCL), cloudy (CLD), or clear (CLR) within a near-solar analysis area. The colour-resolved radial brightness behaviour of the near-solar region is used to define the characteristic property spaces used for STS and IHS. The scoring is based on distance from a region in that property space, using tools of multivariate Gaussian analysis. An external expandable master table of characteristic properties allows continued training of the algorithm. Scores are assigned to the standardized sun-centred image produced from the raw TSI image after a series of calibrations, rotation, and coordinate transformation. We present test results on halo observations and sky type for the first four months of the year 2018, for TSI images obtained at the Southern Great Plains (SGP) ARM site. A detailed comparison of visual and algorithm scores for the month of March 2018 shows that the algorithm is about 90 % reliable in discriminating the four sky types, and identifies 86 % of all visual halos correctly. Numerous instances of halo appearances were identified for the period January through April 2018, with persistence times between 5 and 220  minutes. Varying by month, we found that between 9 % and 22 % of cirrostratus skies exhibited a full or partial 22° halo.

Sylke Boyd et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Sylke Boyd et al.
Model code and software

Haloloop Software S. Boyd, S. Sorenson, S. Richard, M. King, and M. Greenslit https://doi.org/10.5281/zenodo.2226125

Sylke Boyd et al.
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Latest update: 23 Mar 2019
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Short summary
How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, fragments etc. Ice halos form if ice particles in these clouds are in smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, which will allow to explore geographical and seasonal variations in cirrus cloud particle types.
How cirroform clouds affect the radiation balance of the atmosphere depends on their properties,...
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