Machine learning-based seeing estimation and prediction using multi-layer meteorological data at Dome A, Antarctica
Abstract
Atmospheric seeing is one of the most important parameters for evaluating and monitoring an astronomical site. Moreover, being able to predict the seeing in advance can guide observing decisions and significantly improve the efficiency of telescopes. However, it is not always easy to obtain long-term and continuous seeing measurements from a standard instrument such as differential image motion monitor (DIMM), especially for those unattended observatories with challenging environments such as Dome A, Antarctica. In this paper, we present a novel machine learning-based framework for estimating and predicting seeing at a height of 8 m at Dome A, Antarctica, using only the data from a multi-layer automated weather station (AWS). In comparison with DIMM data, our estimate has a root mean square error (RMSE) of 0.18 arcsec, and the RMSE of predictions 20 minutes in the future is 0.12 arcsec for the seeing range from 0 to 2.2 arcsec. Compared with the persistence, where the forecast is the same as the last data point, our framework reduces the RMSE by 37 percent. Our method predicts the seeing within a second of computing time, making it suitable for real-time telescope scheduling.
Cite
@article{arxiv.2304.03587,
title = {Machine learning-based seeing estimation and prediction using multi-layer meteorological data at Dome A, Antarctica},
author = {Xu Hou and Yi Hu and Fujia Du and Michael C. B. Ashley and Chong Pei and Zhaohui Shang and Bin Ma and Erpeng Wang and Kang Huang},
journal= {arXiv preprint arXiv:2304.03587},
year = {2023}
}
Comments
13 pages, 14 figures, accepted for publication in Astronomy and Computing