Forecasting seeing and parameters of long-exposure images by means of ARIMA

TL;DR

This study applies ARIMA models to forecast atmospheric turbulence parameters affecting ground-based astronomical observations, demonstrating improved prediction of image quality metrics based on real data from Mount Shatdzhatmaz.

Contribution

The paper introduces a new ARIMA-based forecasting procedure for atmospheric turbulence parameters and image characteristics in astronomy, validated with real observational data.

Findings

ARIMA models effectively predict short-term atmospheric turbulence variations.

Forecasted image quality metrics have narrower probability distributions.

The technique adequately describes temporal stochastic variations of optical turbulence.

Abstract

Atmospheric turbulence is the one of the major limiting factors for ground-based astronomical observations. In this paper, the problem of short-term forecasting seeing is discussed. The real data that were obtained by atmospheric optical turbulence (OT) measurements above Mount Shatdzhatmaz in 2007--2013 have been analysed. Linear auto-regressive integrated moving average (ARIMA) models are used for the forecasting. A new procedure for forecasting the image characteristics of direct astronomical observations (central image intensity, full width at half maximum, radius encircling 80% of the energy) has been proposed. Probability density functions of the forecast of these quantities are 1.5--2 times thinner than the respective unconditional probability density functions. Overall, this study found that the described technique could adequately describe temporal stochastic variations of the OT power.