Analysis algorithm for sky type and ice halo recognition in all-sky images

Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, Morton Greenslit

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    Halo displays, in particular the 22° halo, have been captured in long time series of images obtained from total sky imagers (TSIs) at various Atmospheric Radiation Measurement (ARM) sites. Halo displays 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 scores images with respect to the presence of 22° halos. Each image is assigned an ice halo score (IHS) for 22° halos, as well as a photographic sky type (PST), which differentiates cirrostratus (PST-CS), partially cloudy (PST-PCL), cloudy (PST-CLD), or clear (PST-CLR) within a near-solar image analysis area. The color-resolved radial brightness behavior of the near-solar region is used to define the discriminant properties used to classify photographic sky type and assign an ice halo score. The scoring is based on the tools of multivariate Gaussian analysis applied to a standardized sun-centered image produced from the raw TSI image, following a series of calibrations, rotation, and coordinate transformation. The algorithm is trained based on a training set for each class of images. We present test results on halo observations and photographic sky type for the first 4 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 photographic 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 min. Varying by month, we found that between 9% and 22% of cirrostratus skies exhibited a full or partial 22° halo.

    Original languageEnglish (US)
    Pages (from-to)4241-4259
    Number of pages19
    JournalAtmospheric Measurement Techniques
    Issue number8
    StatePublished - Aug 6 2019

    Bibliographical note

    Funding Information:
    Acknowledgements. Data were obtained from the Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division. The work was supported by The Undergraduate Research Opportunities Program (UROP) at the University of Minnesota, as well as a grant to the University of Minnesota Morris from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program. Sylke Boyd wishes to thank the University of Minnesota Morris for the generous one-semester release from teaching obligations, allowing for the completion of this work.

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