Nighttime cloud detection, tracking and prediction with All-Sky cameras

Buntin, S ORCID: 0000-0001-8204-2252, Copperwheat, CM ORCID: 0000-0001-7983-8698 and Jermak, HE ORCID: 0000-0002-1197-8501 (2025) Nighttime cloud detection, tracking and prediction with All-Sky cameras. Ras Techniques and Instruments, 4. ISSN 2752-8200

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Abstract

This paper presents a novel method for real-time nighttime cloud detection, tracking, and prediction using all-sky cameras, aimed at enhancing the efficiency of ground-based robotic telescopes. Ground-based telescopes are vulnerable to adverse weather conditions, particularly cloud cover, which can lead to the loss of valuable observation time and potential damage to the telescope. Existing methods for cloud detection have limitations in accuracy, particularly under varying illumination conditions such as gibbous moon phases. To address these challenges, we developed an algorithm that uses the temporal incoherence of image sequences from all-sky cameras. The method computes difference images to highlight moving cloud structures, applies Otsu thresholding to generate binary cloud maps, and uses mathematical morphology techniques to reduce noise from bright stars and other artefacts. Segmented cloud regions are then tracked across successive frames, allowing estimation of a velocity vector and enabling short-term predictions of cloud movement. Our approach achieves reliable cloud detection and tracking, providing predictions up to 15 min into the future - a capability critical for robotic telescopes that rely on look-ahead scheduling. The system was validated against extensive historical data from the Liverpool Telescope's Skycam A and T systems, achieving a false positive rate of approximately 1 per cent and a similar false negative rate, depending on cloud thickness and speed. By improving cloud forecasting and observational scheduling, the system offers a valuable tool for enhancing the operational reliability of robotic telescopes.

Item Type:	Article
Uncontrolled Keywords:	Algorithms; Cloud Detection; Cloud Tracking; astro-ph.IM; astro-ph.IM; 46 Information and Computing Sciences; 40 Engineering; 4603 Computer Vision and Multimedia Computation; Networking and Information Technology R&D (NITRD); 46 Information and Computing Sciences; 40 Engineering; 4603 Computer Vision and Multimedia Computation; Networking and Information Technology R&D (NITRD)
Subjects:	Q Science > QB Astronomy
Divisions:	Astrophysics Research Institute
Publisher:	Oxford University Press (OUP)
Date of acceptance:	22 July 2025
Date of first compliant Open Access:	5 June 2026
Date Deposited:	05 Jun 2026 14:51
Last Modified:	05 Jun 2026 14:51
DOI or ID number:	10.1093/rasti/rzaf034
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28754

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