Model for estimating the astronomical seeing at Dome A, Antarctica.

A model for estimating astronomical seeing at Kunlun Station (Dome A, Antarctica) is proposed. This model is based on the Tatarskii equation, using the wind shear and temperature gradient as inputs, and a seeing model depending directly on the weather data is provided. The seeing and near-ground weather data to build and validate the proposed seeing model were measured at Dome A during the summer of 2019. Two calculation methods were tested from the measured weather data relating the wind shear and temperature gradient to a combination of the two levels for the boundary layer. Both methods performed well, with correlation coefficients higher than 0.77. The model can capture the main seeing trends in which the seeing becomes small when weak wind speed and strong temperature inversion occur inside the boundary layer.

Reliable model to estimate the profile of the refractive index structure parameter (Cn2) and integrated astroclimatic parameters in the atmosphere.

Based on the statistical study of meteorological balloons equipped with thermosondes, a new model that estimates the profile of the refractive index structure constant (Cn2) is proposed. Utilizing temperature, pressure, and wind shear as inputs, this new approach can estimate vertical profiles of Cn2 with 100 m vertical resolution. We used four outer scale models (Thorpe, HMNSP99, Dewan, and our proposed model) on data acquired from Rongcheng (Shandong Peninsula) and Maoming (Guangdong Province) to estimate the Cn2 profiles and compared the results with the measured Cn2 profile. The proposed method outperformed the other three models, yielding an estimation profile that matched well with the measured median Cn2 profiles, with an average relative error generally less than 3.5% and a mean correlation coefficient larger than 0.72 in Maoming, an average relative error generally less than 3.4% and a mean correlation coefficient larger than 0.84 in Rongcheng. The proposed outer scale model also shows good performance in estimating integrated atmospheric parameters.

Simple method to estimate the optical turbulence over snow and ice.

A simple physics-based method for estimating optical turbulence (Cn2) within the surface layer over snow and ice is proposed, using the Tatarski equation with an improved outer scale model. This improved outer scale model mainly requires the calculation of the wind shear and temperature gradients. Based on the measurements from a mobile polar atmospheric parameter measurement system at the Antarctic Taishan Station in 2014, Cn2 was estimated using two methods: the Tatarski equation and the Monin-Obukhov similarity (MOS) theory. Compared with 16 days of measurements from a micro-thermometer, the correlation coefficient of log10(Cn2) estimated by the Tatarski equation is 0.72, which is a slightly more accurate Cn2 variation in trend and magnitude than the MOS theory. The results suggest that this simple method has potential value for the forecasting applications of optical turbulence.

Mesoscale optical turbulence simulations above Tibetan Plateau: first attempt.

The vertical distributions of optical turbulence (C n2 profiles) are a major factor in defining the capabilities of ground-based telescopes and interferometers. As site-testing campaigns are extremely expensive and instruments only provide the local atmospheric parameter, atmospheric modeling might represent an advance prediction result in astronomical sites. The key meteorological parameters and the integrated astroclimatic parameters (Fried parameter r0, seeing ɛ, isoplanatic angle θAO and wavefront coherence time τAO) related to the C n2 profiles above the Tibetan Plateau are investigated for astronomical applications by using the Weather Research and Forecasting (WRF) model. Radiosonde measurements from a field campaign at Lhasa station above the Tibetan Plateau are used to quantify the ability of this model. The results show that the C n2 profile decreases rapidly in the surface layer, increasing with height from the boundary layer to low stratosphere, and decreases gradually in the high free atmosphere. From the whole campaign measurements above the Tibetan Plateau, the mean r0 is 8.64 cm, the mean ɛ is 1.55'', the mean θAO is 0.42'' and the mean τAO is 1.89 ms, and the comparison with the other world's leading observatory sites have been presented. In addition, such as the bias and the root-mean-squared error are used to quantify the performance of the WRF model. In spite of the model performance in reconstructing the meteorological parameters is reasonable in general, the uncertainty in quantifying the C n2 profiles and the integrated parameters are not negligible in some cases. The main results of this study tell us that the WRF model could provide a useful resource to design, monitor the performance of, and even optimize the operation of sophisticated Adaptive Optics (AO) systems.

New $\text{C}_{n}

It is worth highlighting that, for the first time to the best of our knowledge, vertical profiles of atmospheric parameters and $C_n^2$ were measured at Lhasa, south of the Tibetan Plateau, using balloon-borne radiosondes. Based on the measurements, two new statistical models (Lhasa HMN and Lhasa Dewan) for estimating turbulence strength are proposed. Attention has been paid to evaluate the reliability of the two models to reconstruct vertical profiles of $C_n^2$ from a statistical perspective. The statistical analyses presenting the Lhasa HMN model are accompanied with lower bias, root mean square error (RMSE), and bias-corrected RMSE ($\sigma$) than those of the Lhasa Dewan model, which implies the Lhasa HMN model can better reveal the nature of turbulence characteristics of Lhasa influenced by unique local weather conditions. In addition, the comparison between the Lhasa HMN model and measurements in calculating integrated astroclimatic parameters is carried out, and the result suggests that the performance of the Lhasa HMN model is reliable and satisfactory. The new reliable $C_n^2$ model offers new insight into the characteristics of optical turbulence at Lhasa and provides support for pursuing astronomical site selection in the Tibetan Plateau.

Adaptive niche-genetic algorithm based on backpropagation neural network for atmospheric turbulence forecasting.

Because systematic direct measurements of the refractive index structure constant ($C_n^2$Cn2) are not available for many climates and seasons, we developed an indirect method to forecast optical turbulence. The $C_n^2$Cn2 was estimated from a backpropagation neural network optimized by an adaptive niche-genetic algorithm. The estimated result was validated against the corresponding six-day $C_n^2$Cn2 data from a field campaign of the 30th Chinese National Antarctic Research Expedition. We also compared the correlation coefficient, root mean square error, and systematic error bias of the proposed model with the weather research and forecasting model. The results suggest that our model shows better correlation and reliably estimates $C_n^2$Cn2.