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

Abstract

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.