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Atmospheric seeing is one of the most important parameters for evaluating andmonitoring an astronomical site. Moreover, being able to predict the seeing inadvance can guide observing decisions and significantly improve the efficiencyof telescopes. However, it is not always easy to obtain long-term andcontinuous seeing measurements from a standard instrument such as differentialimage motion monitor (DIMM), especially for those unattended observatories withchallenging environments such as Dome A, Antarctica. In this paper, we presenta novel machine learning-based framework for estimating and predicting seeingat a height of 8 m at Dome A, Antarctica, using only the data from amulti-layer automated weather station (AWS). In comparison with DIMM data, ourestimate has a root mean square error (RMSE) of 0.18 arcsec, and the RMSE ofpredictions 20 minutes in the future is 0.12 arcsec for the seeing range from 0to 2.2 arcsec. Compared with the persistence, where the forecast is the same asthe last data point, our framework reduces the RMSE by 37 percent. Our methodpredicts the seeing within a second of computing time, making it suitable forreal-time telescope scheduling.

Machine learning-based seeing estimation and prediction using multi-layer meteorological data at Dome A, Antarctica