Atmospheric turbulence forecasting with a General Circulation Model for Cerro Paranal

TL;DR

This paper evaluates a fast, GCM-based forecast model for atmospheric turbulence at Cerro Paranal, demonstrating high correlation with stereo-SCIDAR measurements and potential for improved astronomical observation scheduling.

Contribution

It introduces a rapid, statistically consistent turbulence forecast model based on a GCM, suitable for real-time applications and remote site characterization.

Findings

Correlation of 0.98 between model and measurements for turbulence profile

Correlation of 0.64 for free atmosphere seeing

Model effectively forecasts turbulence evolution over nights

Abstract

In addition to astro-meteorological parameters, such as seeing, coherence time and isoplanatic angle, the vertical profile of the Earth's atmospheric turbulence strength and velocity is important for instrument design, performance validation and monitoring, and observation scheduling and management. Here we compare these astro-meteorological parameters as well as the vertical profile itself from a forecast model based on a General Circulation Model from the European Centre for Median range Weather Forecasts and the stereo-SCIDAR, a high-sensitivity turbulence profiling instrument in regular operation at Paranal, Chile. The model is fast to process as no spatial nesting or data manipulation is performed. This speed enables the model to be reactive based on the most up to date forecasts. We find that the model is statistically consistent with measurements from stereo-SCIDAR. The correlation of the median turbulence profile from the model and the measurement is 0.98. We also find that the distributions of astro-meteorological parameters are consistent. We compare contemporaneous measurements and show that the free atmosphere seeing, isoplanatic angle and coherence time have correlation values of 0.64, 0.40 and 0.63 respectively. We show and compare the profile sequences from a large number of trial nights. We see that the model is able to forecast the evolution of dominating features. In addition to smart scheduling, ensuring that the most sensitive astronomical observations are scheduled for the optimum time, this model could enable remote site characterisation using a large archive of weather forecasts and could be used to optimise the performance of wide-field AO system.