| 23 Feb 2026
A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications
Abstract. Clouds play a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover and can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All-Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information.
This paper introduces an approach to performing cloud cover analysis by means of image segmentation, designed specifically to work with a new type of all-sky camera operated by the Atmospheric Radiation Measurement (ARM) program, which replaces an older camera model used for multiple decades. The objective of the algorithm is to provide continuity to cloud cover estimates from all-sky camera images by the ARM program, and perform well at multiple sites of deployment.
The paper is well-written, and the visualizations are helpful (but image resolution needs to be improved for some of the figures, see line-by-line comments). The algorithm’s performance is assessed both quantitatively (using a test dataset) and more qualitatively (by comparison with other collocated measurement data), and this performance seems fit for purpose.
My main critique of the current paper is that it reads more as an algorithm description document than a scientific paper. The scientific value of the algorithm and its output is clear to me, but I think the paper could improve in placing the algorithm in context of other approaches discussed in the literature and how the performance compares to those. Specifically, the method to classify cloudiness operates on a per-pixel basis, which is likely inferior to segmentation algorithms that consider the entire image at once (e.g. a convolutional neural network). Although it is mentioned that computational efficiency is required for generating the algorithm’s output in near real-time, it is not exactly clear what the limiting factors are and whether the simplified approach is justified: especially when little to no successfully implemented alternative approaches are mentioned. It is thus also difficult for the reader to assess the performance of the algorithm. As an example, the paper states that “Clear sky pixels are properly classified at an impressive rate of 91%”, but how can we possibly say that this is impressive if no reference performance is given?
Some other broader comments/questions I have:
Line-by-line comments:
Figure 1:
Line 102: should “exists” not be “exist” ?
Figure 2:
Line 162: “bogus cloud cover” is more exact terminology available to describe this?
Line 178: “mis-alignment” spelled as “misalignment” elsewhere in the paper
Equation 2: Might be helpful to state the units of the angles here?
Line 244: one symbol is italic, the other isn’t. And is “solar zenith angles” here correct?
Line 260: Shouldn’t all the numbers here be written as 75th, 95th etc?
Figure 5: