Data processing and data products from 2017 to 2019 campaign of astronomical site testing at Ali, Daocheng and Muztagh-ata

Cao, Zihuang; Hao, Jinxin; Lu, Feng; Jones, H. R. A.; Li, Jian; Xu, Jing; Liu, Liyong; Song, Tengfei; Wang, Jianfeng; Chen, Hualin; Xue, Yan-Jie; Shen, Hao; Zhao, Yi; Qi, Zhaoxiang; Shi, Jianrong; Tian, Yuan; Wang, Zheng; Wang, Xia; Hou, Jing; Bai, Jinming; Yang, Ji; Xu, Zhiyong; Yao, Yulong; Yin, Jian-Tao; Esamdin, Ali; Pu, Guang-Xin; Wei, Peng; Bai, C. H.; Feng, Guojie; Ma, Lihua; Zhang, Xuan; Liu, Yu; Pei, Chong; Shen, Zhenxing; Jiang, Yingde; Jian-feng, Tian; Zeng, Xingguo; Hou, Zhi-Gang; Xu, Yang; Zhang, Junbo; Liao, S.; Fan, Dongwei; Xu, Yunfei; Xiong, Jian-Ping; Yan, Tai-Sheng; Zhang, Xi; Li, Changhua; Tao, Yihan; Yu, Sheng‐Xiang; Cao, Zhi-Song; Wang, Hongshuai

doi:10.1088/1674-4527/20/6/82

Cited by 17 publications

(5 citation statements)

References 22 publications

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“…Here, the sky area with zenith angles smaller than 44°.7 is defined as the inner circle, and the sky area with zenith angles between 44°.7 and 65°is defined as the outer circle. An example of the four definitions of all-sky images can be seen in Figure 9 of Cao et al (2020a).…”

Section: Cloud Amount Measurementmentioning

confidence: 99%

“…The consistency of the cloud measurement between the satellite and ASC data is affected by many factors (Cao et al 2020a), such as the observation band, the field of view, the processing algorithm (Wang et al 2022), and the different detection capability for clouds with different heights. In the months from March to July, the heights of cloud tops were obviously higher than in the other months, and the discrepancy in observing nights between satellite data and our all-sky images was larger than in other months.…”

Section: Cloud Amount Statisticsmentioning

confidence: 99%

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Site-testing at the Muztagh-ata Site V. Nighttime Cloud Amount during the Last Five Years

Feng

Pu³

et al. 2023

Res. Astron. Astrophys.

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The clarity of nights is the major factor that should be carefully considered for optical/infrared astronomical observatories in site-testing campaigns. Cloud coverage is directly related to the amount of time available for scientific observations at observatories. In this article, we report on the results of detailed night-time cloud statistics and continuous observing time derived from ground-based all-sky cameras at the Muztagh-ata site from 2017 to 2021. Results obtained from acquisition data show that the proportion of the annual observing time at the Muztagh-ata site is 65%, and the best period with the least cloud coverage and longer continuous observing time is from September to February. We made a comparison of the monthly mean observing nights obtained from our all-sky cameras and CLARA dataset, results show that the discrepancy between them may depend on the cloud top heights. On average, this site can provide 175 clear nights and 169 nights with at least 4 hours of continuous observing time per year.

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Section: Cloud Amount Measurementmentioning

confidence: 99%

Section: Cloud Amount Statisticsmentioning

confidence: 99%

Site-testing at the Muztagh-ata Site V. Nighttime Cloud Amount during the Last Five Years

Feng

Pu³

et al. 2023

Res. Astron. Astrophys.

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“…The cloud amount data is from all sky camera equiped at our site, the all sky were images were classified into four types by manual checking: "covered" which indicates overcast, "clear" which indicates clear sky, "inner" means partly cloudy but the total cloud amount is not more than 50%, "outer" means no cloud in the zenith area but a small amount of clouds distributed in other area. Detailed all sky images classification and conduct method can be seen in Cao et al (2020). In the lower panel of Figure 5, the four colors represent different cloud cover classifications, red for "covered", blue for "clear", yellow for "inner" and green for"outer".…”

Section: Pwv From Ecwmfmentioning

confidence: 99%

Site-testing at Muztagh-ata site. IV. Precipitable Water Vapor

Esamdin

et al. 2022

PASP

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This is the fourth article in a series studying on the site-testing at Muztagh-ata site. The results of the characterization of precipitable water vapor (PWV) in the atmospheric column at Muztagh-ata site are presented. This paper studies the temporal variability of PWV, acquired from remote sensing data sets and validated by limited radiometers data, spanning a period from 2018 to 2019. The clear day PWV annual median values are 2.28 mm and 2.37 mm for 2018 and 2019 respectively. The results show that the driest seasons at Muztagh-ata site are from October to March and there are about 60 ∼ 70 days with daily median PWV less than 2 mm in this period. Atmospheric transmission in mid-infrared bands calculated by AM model confirms the IR observation potential of Muztagh-ata site.

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“…In the field of astronomy, cloud coverage analysis was usually conducted by manual observation. For instance, some outstanding telescopes and observation sites, such as Thirty Meter Telescope (TMT) [3], Large Optical/Infrared Telescope (LOT) [4], and Dome A [5], employed manual observation methods to estimate night cloud distribution. However, manual observation is to identify and interpret cloud images by visual analysis.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of All-sky Nighttime Cloud Images Based on Machine Learning

Zhong,

Du,

et al. 2024

Preprint

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Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

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Data processing and data products from 2017 to 2019 campaign of astronomical site testing at Ali, Daocheng and Muztagh-ata

Abstract: Based on previous site-testing and satellite cloud data, Ali, Daocheng and Muztagh Ata have been selected as candidate sites for a large optical/infrared telescope in China. We present the data collection, processing, management and quality

Cited by 17 publications

References 22 publications

Site-testing at the Muztagh-ata Site V. Nighttime Cloud Amount during the Last Five Years

Site-testing at the Muztagh-ata Site V. Nighttime Cloud Amount during the Last Five Years

Site-testing at Muztagh-ata site. IV. Precipitable Water Vapor

Automatic Classification of All-sky Nighttime Cloud Images Based on Machine Learning

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