Evaluation of filtering techniques to increase the reliability of weather forecasts for ground-based telescopes

Abstract

In this contribution we evaluate the impact of filtering techniques in enhancing the accuracy of forecasts of optical turbulence and atmospheric parameters critical for ground-based telescopes. These techniques make use of the data continuously provided by the telescope sensors and instruments to improve the performances of real-time forecasts which have an impact on the telescope operation. In previous works we have already shown how a mesoscale high-frequency forecast (Meso-NH [ 1, 2 ] and Astro-Meso-Nh models [ 3, 4 ]) can produce reliable predictions of different atmospheric parameters [ 5 ] and the optical turbulence [ 4 ]. The mesoscale forecast has an advantage on the global model in having a better implementation of the physical atmospheric processes, including turbulence, and produces an output with greater spatial resolution (up to 100m or beyond). Filtering techniques that make use of the real-time sensor data at the telescope may help in removing potential biases and trends which have an impact on short term mesoscale forecast and, as a consequence, may increase the accuracy of the final output. Given the complexity and cost of present and future top-class telescope installations, each improvement of forecasts of future observing conditions will definitely help in better allocating observing time, especially in queue-mode operation, and will definitely benefit the scientific community in medium-long term.

Multistep ahead atmospheric optical turbulence forecasting for free-space optical communication using empirical mode decomposition and LSTM-based sequence-to-sequence learning

“…The ARIMA model is trained on a huge quantity of historical data to simulate the temporal dependency of atmospheric optical turbulence, and then the historical data are used to predict short-term atmospheric optical turbulence [29]. The filter method takes into account, simultaneously, the forecast obtained with non-hydrostatical mesoscale atmospheric models and the real-time measurements to help in removing potential biases and trends which have an impact on short-term atmospheric optical turbulence forecasting [30,31]. Autoregressive models are revealed to be extremely efficient in improving the forecast accuracy on short time scales and usually have a simple model structure, quick calculation speed, and good interpretation capability.…”

Atmospheric turbulence forecasting using two-stage variational mode decomposition and autoregression towards free-space optical data-transmission link

“…Since atmospheric turbulence has a certain short-term correlation, using this property can greatly improve the short-term atmospheric turbulence forecast accuracy and temporal resolution [45,46]. Several researchers proposed using real-time measurements and filtering techniques to improve the forecast performances on shorter time scales [47,48]. Despite the unprecedented prediction accuracy achieved, there is still much room for development.…”