Atmospheric Seeing Research Articles

Atmospheric Turbulence Parameters Deduced from VerticalProfile of Cn2

The proposed framework can capture realistic spatiotemporal variations in atmospheric turbulence parameters based on local meteorological conditions, such as Fried's parameter r0, isoplanatic angles θ0, seeing ε, scintillation rate , and the wavefront coherence time τ0. Those parameters have been deduced from the refractive index structure parameter Two Iraqi locations were compared, specifically the middle and southern borders, and atmospheric turbulence parameters relevant to astronomical observation were measured, lines were drawn at latitudes 34.26° and 28.31°, respectively. The methodology included collecting meteorological data from both sites and performing numerical simulations based on atmospheric turbulence parameters. According to the criteria of air turbulence, the results showed that the middle of Iraq was better than the southern location for astronomical observation.

Simulating Vertical Profiles of Optical Turbulence at the Special Astrophysical Observatory Site

In this paper, we used meteorological data to simulate vertical profiles of optical turbulence at the Special Astrophysical Observatory (SAO) (Russia, 43°40′19″ N 41°26′23″ E, 2100 m a.s.l.), site of the 6 m Big Telescope Alt-azimuthal. For the first time, the vertical profiles of optical turbulence are calculated for the SAO using ERA-5 reanalysis data. These profiles are corrected using DIMM measurements as well as estimations of atmospheric boundary layer heights. We may note that the method basically reconstructs the most important features of the shape of the measured profile under clear sky. Atmospheric turbulent layers were identified, and the strength of optical turbulence in these layers was estimated. The model hourly values of seeing corresponding to the obtained vertical profiles range from 0.40 to 3.40 arc sec; the values of the isoplanatic angle vary in the range from 1.00 to 3.00 arc sec (at λ = 500 nm). The calculated median of seeing is close to 1.21 arc sec. These estimations are close to the measured median of seeing (1.21 arc sec).

Limitations of beam-control compensation

In this paper, we use wave-optics simulations to explore the limitations of beam-control compensation. We evaluate performance in terms of the normalized power in a diffraction-limited bucket for the cases of no beam-control compensation, perfect phase compensation, and perfect full-field compensation. From these results, we are able to arrive at the following conclusions: (1) without any form of beam-control compensation, performance begins to degrade when D/r0 > 1; (2) with perfect phase compensation, performance begins to degrade when D/r0 > 1 and (λ/r0)/θ0 > 1; and (3) with perfect full-field compensation, performance begins to degrade when D/r0 > 1 and (λ/D)/θ0 > 1. Here, D is the aperture diameter, r0 is the Fried parameter, λ is the wavelength, and θ0 is the isoplanatic angle. We show (1)–(3) to be true for varying aperture diameters, uniformly distributed turbulence, and varying turbulence profiles. These findings will inform the development of future laser systems that need to sense and correct for the effects of atmospheric turbulence.

Optical turbulence simulations over the Tibetan Plateau based on a single-column model with high-order closure

Optical turbulence limits the maximum resolution of ground-based telescopes and leads to image degradation. The use of atmospheric numerical techniques to forecast optical turbulence is crucial for observation scheduling and optimization of adaptive optic systems. Current research methods for forecasting optical turbulence are primarily based on mesoscale models with turbulence closure techniques to parameterize the key terms required for C n 2 calculations under the assumption of atmospheric quasi-steady-state balance, and then the integrated astroclimatic parameters related to C n 2 profile can be obtained. In this study, we propose what we believe to be a novel approach to forecast C n 2 using a boundary layer parameterization based on higher-order turbulence closure in the single-column framework of the CLUBB model. Compared to mesoscale models, the CLUBB model serves as a single-column model, which simplifies modifications and reduces compilation time, and is more conducive to testing the performance of C n 2 parameterization scheme. In the design of C n 2 parameterization scheme, we consider a more complete physical process rather than omitting certain terms to obtain a steady-state solution. The performance of the model is evaluated using measurements obtained during a field campaign conducted at Da Qaidam site above the Tibetan Plateau. The results show that the model is able to capture typical features of the C n 2 profile evolution under convective conditions. Comparison of the model with contemporaneous sounding measurements and quantification of the model’s performance using statistical operators demonstrate the statistical agreement between simulations and measurements. In terms of atmospheric seeing, we can observe a bias of 0.01 and a root-mean-square error (RMSE) of 0.31 without any model calibration, which outperforms the results of previous mesoscale modeling studies. In addition, the new parameterization scheme is also compared with two representative C n 2 algorithms previously used in the mesoscale models, with some improvements observed. The potential demonstrated by this approach is expected to bring greater value and advancement to the research of three-dimensional forecasting of optical turbulence in the future by coupling with the mesoscale model.

Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization

Optical telescopes are an important tool for acquiring optical information about distant objects, and resolution is an important indicator that measures the ability to observe object details. However, due to the effects of system aberration, atmospheric seeing, and other factors, the observed image of ground-based telescopes is often degraded, resulting in reduced resolution. This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function (PSF) of the telescopic system and the image super-resolution (SR) network. To improve the speed of image reconstruction, we designed a generative adversarial net (LCR-GAN) with light parameters, which is much faster than the latest unsupervised networks. To reconstruct the PSF trained by the network in the optical path, a phase mask is introduced. It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network. The results of simulation and verification experiments show that compared with the pure deep learning method, the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

Stellar angular momentum of intermediate-redshift galaxies in MUSE surveys

We quantify the stellar rotation of galaxies by computing the λR parameter, a proxy for the stellar angular momentum in a sample of 106 intermediate-redshift galaxies (0.1 < z < 0.8). The sample is located in the CANDELS/GOODS-S and CANDELS/COSMOS regions, and it was observed by various MUSE surveys. We created spatially resolved stellar velocity and velocity dispersion maps using a full-spectrum fitting technique, covering spatially ∼2Re for the galaxies. The sample spans stellar masses from ∼107.5 M⊙ to 1011.8 M⊙ with star formation rates (SFRs) from log10(SFR)≈ − 3 M⊙ yr−1 to ≈1.7 M⊙ yr−1 over a range of 6 Gyr in cosmic time. We studied how the atmospheric seeing, introduced by the instrumental point spread function (PSF), affects the measured spin parameter, and we applied corrections when pertinent. Through the analysis of the λR − ϵ diagram, we note that the fraction of round and massive galaxies increases with redshift. We did not measure any galaxy with λR < 0.1 in the sample, and we found only one potential (but uncertain) low-mass slow rotator at z ∼ 0.3, more similar to the z = 0 low-mass slow rotators characterized by counter-rotation than to massive ellipticals. Moreover, we do not see an evident evolution or trend in the stellar angular momentum with redshift. We characterized the galaxy environment using two different indicators: a local estimator based on the Voronoi tesselation method, and a global estimator derived by the use of the friends-of-friends (FoF) algorithm. We find no correlation between the environment and λR given that we are not probing dense regions or massive galaxy structures. We also analysed the kinematic maps of the sample finding that about 40% of the galaxies are consistent with being regular rotators (RRs), having rotating stellar discs with flat velocity dispersion maps, while ∼20% have complex velocity maps and can be identified as non-regular rotators in spite of their λR values. For the remaining galaxies the classification is uncertain. As we lack galaxies with λR < 0.1 in the sample, we are not able to identify when galaxies lose their angular momentum and become slow rotators within the surveyed environments, area, and redshift range.

Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies

ABSTRACT We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million targets, covering 14 000 deg2 of the sky, with redshifts in the range 0.2 < z < 1.35. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without fNL and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $f_{\rm NL} = 34^{+24(+50)}_{-44(-73)}$ at 68 per cent (95 per cent) confidence. We apply a series of robustness tests (e.g. cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power spectrum and degrades the fNL constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid overcorrection, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the fNL uncertainty.

Reference optical turbulence characteristics at the Large Solar Vacuum Telescope site

Abstract Large ground-based solar telescopes are equipped with adaptive optics systems to correct wavefront distortions induced in the turbulent atmosphere. The design of the adaptive optics system strongly depends on the vertical profiles of the optical turbulence. In particular, the characteristics of the optical turbulence determine the design of tomographic adaptive optics systems, which provide image correction within a wide field of view. In the article, a new method to estimate reference optical turbulence characteristics from Era-5 reanalysis assimilated data is presented. This method is based on the dependence of the air refractive index structure constant $C_n^2$ on the vertical shears of wind speed as well as the outer scale of turbulence L0. The L0 parameter is estimated by minimization of the dispersion between the modeled and measured values of the refractive index structure constant $C_n^2$ within the surface layer. For the first time, parametrization coefficients and reference profiles of optical turbulence averaged for the period 1940–2022 are calculated for the Large Solar Vacuum Telescope (LSVT) site. The calculated optical turbulence profiles are representative; these profiles correspond to typical changes of the measured values of the Fried parameter, the isoplanatic angle, and the outer scale of turbulence at the LSVT site. The model turbulence profiles are verified taking into account the Shack–Hartmann wavefront sensor measurements at the LSVT. The higher accuracy of estimation of the optical turbulence characteristics makes it possible to refine parameters relevant to the LSVT adaptive optics system. The obtained results can be used in order to develop high-resolution solar adaptive optics technologies as applied to ground-based telescopes including those using the principles of atmospheric tomography.

MUST dome design based on dome seeing quantitative evaluation

ABSTRACT The Multiplexed Survey Telescope (MUST) project is led by Tsinghua University, which entrusted the European Industrial Engineering (EIE) GROUP with the design, manufacture, and assembly of the dome. Located on Saishiteng Mountain in Qinghai Province, MUST benefits from exceptional atmospheric seeing conditions etc. Local dome seeing may be comparable to atmospheric seeing and requires careful consideration. This research, based on numerical simulations, focuses on refining the dome structure and temperature regulation strategies to achieve optimal dome seeing. The existing simulations only consider nighttime dome seeing and overlook the impact of daytime dome heating on nighttime conditions. The Computational Fluid Dynamics (CFD) part mostly relies on a steady-state k − ε model, which cannot simulate transient processes or capture optical turbulence. In this study, a comprehensive multiphysics field coupling simulation was conducted, encompassing radiation heat transfer, fluid heat transfer, CFD, and ray tracing. Simulations include both daytime and nighttime scenarios, taking into account the daytime heating of the dome due to solar irradiation, as well as dome seeing under natural ventilation at night. The CFD utilizes the large eddy simulation model, enabling three-dimensional transient simulation and the simulation of optical turbulence. Ultimately, the broadening of the point spread function was statistically analysed after a certain integration time, facilitating a quantitative evaluation of dome seeing. This numerical simulation approach is closer to real world conditions, improving simulation accuracy and addressing the shortcomings of existing simulations. Some qualitative conclusions are consistent with practical engineering experience. In the end, the dome seeing was successfully regulated to 0.21 arcsec, meeting the observational requirements.

Seeing and turbulence profile simulations over complex terrain at the Thai National Observatory using a chemistry-coupled regional forecasting model

Abstract This study utilized advanced numerical simulations with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to predict anticipated astronomical seeing conditions at the Thai National Observatory (TNO). The study evaluated the effects of both gas-phase and aerosol-phase chemical processes in the Earth’s atmosphere, along with the impact of spatial and temporal resolution on model performance. These simulations were validated against measurements from the Differential Image Motion Monitor (DIMM) and the Slope Detection and Ranging (SLODAR) technique. Due to the inherent temporal variability of the DIMM observations, a 24-h moving average window was applied to both DIMM data and WRF-Chem model outputs. This reduced the percentage root-mean-square error in the comparison between the two data sets from 23 per cent to 11 per cent and increased the correlation coefficient from 0.21 to 0.59. Chemistry played a minor role during the study period, contributing 3.49 per cent to astronomical seeing. However, it did affect the model’s accuracy. Additionally, the study revealed that higher spatial and temporal resolution simulations did not necessarily improve the model’s accuracy. When compared to SLODAR observations of the refractive index structure constant (Cn2dh), the simulations captured altitude variations within ±25 per cent above 5 km and 25–50 per cent below 5 km. Dome seeing also played a role, contributing to around 90 per cent or more in the lowest altitude layer. The results emphasized the significance of seeing predictions in providing valuable insights into complex atmospheric phenomena and how to mitigate the effects of atmospheric turbulence on telescopes.

A comparison of next-generation turbulence profiling instruments at Paranal

ABSTRACT A six-night optical turbulence monitoring campaign has been carried at Cerro Paranal observatory in 2023 February and March to facilitate the development and characterization of two novel atmospheric site monitoring instruments – the ring-image next-generation scintillation sensor (RINGSS) and 24-h Shack Hartmann image motion monitor (24hSHIMM) in the context of providing optical turbulence monitoring support for upcoming 20–40 m telescopes. Alongside these two instruments, the well-characterized Stereo-SCIDAR and 2016-MASS-DIMM were operated throughout the campaign to provide data for comparison. All instruments obtain estimates of optical turbulence profiles through statistical analysis of intensity and wavefront angle-of-arrival fluctuations from observations of stars. Contemporaneous measurements of the integrated turbulence parameters are compared and the ratios, bias, unbiased root mean square error, and correlation of results from each instrument assessed. Strong agreement was observed in measurements of seeing, free atmosphere seeing, and coherence time. Less correlation is seen for isoplanatic angle, although the median values agree well. Median turbulence parameters are further compared against long-term monitoring data from Paranal instruments. Profiles from the three small-telescope instruments are compared with the 100-layer profile from the stereo-SCIDAR. It is found that the RINGSS and SHIMM offer improved accuracy in characterization of the vertical optical turbulence profile over the MASS-DIMM. Finally, the first results of continuous optical turbulence monitoring at Paranal are presented which show a strong diurnal variation and predictable trend in the seeing. A value of 2.65 arcsec is found for the median daytime seeing.

Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory

In the present article, we study the possibilities of machine learning for the estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24″ N, 66∘53′47″ E) using only Era-5 reanalysis data. Seeing is usually associated with the integral of the turbulence strength Cn2(z) over the height z. Based on the seeing measurements accumulated over 13 years, we created ensemble models of multi-layer neural networks under the machine learning framework, including training and validation. For the first time in the world, we have simulated optical turbulence (seeing variations) during night-time with deep neural networks trained on a 13-year database of astronomical seeing. A set of neural networks for simulations of night-time seeing variations was obtained. For these neural networks, the linear correlation coefficient ranges from 0.48 to 0.68. We show that modeled seeing with neural networks is well-described through meteorological parameters, which include wind-speed components, air temperature, humidity, and turbulent surface stresses. One of the fundamental new results is that the structure of small-scale (optical) turbulence over the Maidanak Astronomical Observatory does not depend or depends negligibly on the large-scale vortex component of atmospheric flows.

A Multifaceted Exploration of Atmospheric Turbulence and Its Impact on Optical Systems: Structure Constant Profiles and Astronomical Seeing

This study emphasizes the importance of conducting a comprehensive analysis considering the Cn2 atmospheric parameter for a reliable assessment of the seeing value. It highlights the use of the ECMWF ERA5 model to simulate pressure, temperature, and humidity profiles, enabling the evaluation of optical degradation. Accurate modeling and monitoring of temperature and humidity variables are emphasized for precise data in optical communication and astronomical observations. It also discusses the significance of optical turbulence models in understanding the impact of atmospheric turbulence on optical system performance. The validation of the selected Cn2 models is thoroughly analyzed. To evaluate the impact of the boundary layer on seeing conditions, three factors are considered. First, ERA5 data is used to simulate surface Cn2 values using the PAMELA model. Second, typical Cn2 values for specific dates in Redu are obtained. Finally, the thickness of the boundary layer, denoted as hbl, is calculated. This finding highlights the significant influence of the boundary layer on atmospheric seeing conditions and by incorporating Ibl into the models, a more accurate representation of the effects of the boundary layer on seeing quality is achieved.

An efficient astronomical seeing forecasting method by random convolutional Kernel transformation

Astronomical seeing is the primary indicator of the observation quality of large-scale optical telescopes. Forecasting astronomical seeing is an essential and challenging task in the operation of large optical telescopes, as it can help observers more efficiently arrange observation tasks as well as providing hints for mechanism optimization of optical telescopes. In this paper, we propose an efficient data-driven astronomical seeing forecasting method by leveraging the environment monitoring data from multiple sensors deployed with optical telescopes. The basic idea is to use a large number of random yet fixed convolution kernels to extract features from the original data, and then train a simple tree-based predictor based on these features. In particular, the raw monitoring data is transformed by differential operations, enabling a more explicit representation of spatial and temporal features; then an elaborate convolution mechanism, including the convolution kernel and the pooling operator, is designed to extract a variety set of features of the input time series, which serve as the foundation for constructing the time series forecasting model. In contrast to existing deep learning models, there is no need to calculate and back-propagate the gradients during the training procedure, making the proposed method much more efficient. We collect real-world monitoring data from Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), one of the world’s largest optical reflecting telescopes, and conduct extensive empirical evaluations. The experimental results demonstrate that the proposed method can achieve state-of-the-art prediction accuracy, i.e., 0.1125 in MAE, 0.0252 in MSE, and 0.0322 in MAPE, outperforming most deep-learning-based forecasting models. Moreover, it surpasses both machine learning and deep learning approaches in terms of training efficiency with a significant margin.

On-Earth Observation of Low Earth Orbit Targets through Phase Disturbances by Atmospheric Turbulence

This paper addresses the challenges of ground-based observation of Low Earth Orbit (LEO) targets using on-Earth telescopes and proposes solutions for improving image quality and tracking fast-moving objects under atmospheric turbulence conditions. The study investigates major challenges, including atmospheric turbulence-induced aberrations, blurring, telescope platform vibrations, and spatial variations caused by the target motion. A scenario simulation is conducted considering practical limitations, such as feasible time slots for monitoring and the number of available frames within the Field of View (FoV). The paper proposes a novel method for detecting LEO targets using Harris corner features and matching adjacent images upon the corrected frames by Adaptive Optics (AO), with a 38% reduction in the Mean Squared Error (MSE) achieved for certain frames within the isoplanatic angle. Additionally, a refinement strategy for deblurring images is proposed, combining the post-processing with Singular Value Decomposition (SVD) filtering; with a proper filtering factor (β=0.1), an almost complete collection of 14 corner nodes can be detected. The feasibility of continuously tracking objects with uncontrolled attitudes, such as space debris, is successfully demonstrated with continuous monitoring of certain features. The proposed methods offer promising solutions for ground-based observation of LEO targets, providing insights for further research and application in this field.

Research on piston error sensing for segmented mirrors under atmospheric turbulence.

Large aperture ground-based segmented telescopes typically use electrical edge sensors to detect co-phase errors. However, complex observing environments can lead to zero-point drift of the edge sensors, making it challenging to maintain the long-term co-phase of the segmented primary mirror using only edge sensors. Therefore, employing optical piston error detection methods for short-term calibration of edge sensors can address the issue of zero-point drift in the sensors. However, atmospheric turbulence can affect calibration accuracy based on the observational target. To achieve high-precision calibration of electrical edge sensors, this study investigates the impact of atmospheric turbulence on optical piston error detection. Based on simulated results, it is found that the actual measured piston error in the presence of atmospheric turbulence is the difference between the average phases of the two segments. Subsequently, optical piston error detection experiments were conducted in a segmented mirror system under simulated turbulent conditions with varying turbulence intensities. Experimental studies have shown that the detection accuracy of the optical method is almost the same as without turbulence when using a detection aperture size that is 0.82 times the atmospheric coherence length and an exposure time of at least 40 ms. The root mean square of the cross-calibration is better than 3nm. These experimental results indicate that under conditions of good atmospheric seeing, the optical piston error detection method can meet the short-term calibration requirements of edge sensors by setting reasonable detection area size and exposure time. It may even be possible to directly use optical detection methods to replace edge sensors for real-time detection of piston errors.

Benefits of Intelligent Fuzzy Controllers in Comparison to Classical Methods for Adaptive Optics

Adaptive Optics (AO) systems have been developed throughout recent decades as a strategy to compensate for the effects of atmospheric turbulence, primarily caused by poor astronomical seeing. These systems reduce the wavefront distortions using deformable mirrors. Several AO simulation tools have been developed, such as the Object-Oriented, MATLAB, and Adaptive Optics Toolbox (OOMAO), to assist in the project of AO. However, the main AO simulators focus on AO models, not prioritizing the different control techniques. Moreover, the commonly applied control strategies in ground-based telescopes are based on Integral (I) or Proportional-Integral (PI) controllers. This work proposes the integration of OOMAO models to Simulink to support the development of advanced controllers and compares traditional controllers with intelligent systems based on fuzzy logic. The controllers were compared in three scenarios of different turbulence and atmosphere conditions. The simulations were performed using the characteristics/parameters of the Southern Astrophysical Research (SOAR) telescope and assessed with the Full Width at Half Maximum (FWHM), Half Light Radius (HLR), and Strehl ratio metrics to compare the performance of the controllers. The results demonstrate that adaptive optics can be satisfactorily simulated in OOMAO adapted to Simulink and thus further increase the number of control strategies available to OOMAO. The comparative results between the MATLAB script and the Simulink blocks designed showed a maximum relative error of 3% in the Strehl ratio and 1.59% in the FWHM measurement. In the assessment of the control algorithms, the fuzzy PI controller reported a 25% increase in the FWHM metrics in the critical scenario when compared with open-loop metrics. Furthermore, the fuzzy PI controller outperformed the results when compared with the I and PI controllers. The findings underscore the constraints of conventional control methods, whereas the implementation of fuzzy-based controllers showcases the promise of intelligent approaches in enhancing control performance under challenging atmospheric conditions.

Astronomical seeing and ground-level optical turbulence at Delingha observatory on the northern Tibetan Plateau

ABSTRACT This paper presents a comprehensive site survey of the Delingha site on the northern Tibetan Plateau from 2012 to 2014. In this campaign, we investigated 24 h continuous astronomical seeing, meteorological properties, and $C_{n}^{2}$ in the ground layer (GL). The results show that the median seeing is 3.46 arcsec during the day and 1.80 arcsec during the night. The best nighttime seeing occurs in summer (1.66 arcsec), while the worst occurs in autumn (1.96 arcsec). Seeing exhibits obvious diurnal behaviour, with the worst seeing occurring around mid-day (11:00 to 14:00, Beijing time), and the best seeing just before sunset (18:00 to 20:00, Beijing time). Moreover, the Delingha site has good meteorological properties for astronomical observations during the measurement period, especially the low median wind speed (2.8 m s−1) and great temperature inversion (day: −0.3 K, night: 0.6 K). As for the vertical distribution of $C_{n}^{2}$, the exponential h−0.46 dependence of $C_{n}^{2}$ varying with height in the GL is also obtained, and its power index is significantly related to the seasons. Furthermore, we calculated the contribution of optical turbulence in the GL to the whole atmosphere. Turbulence within the GL contributes significantly in summer, and its contribution can reach more than 35 per cent. Finally, we present the complete dependence analysis of seeing on meteorological parameters at the Delingha site. The results show that the best seeing tends to occur when the temperature inversion is sufficiently large, but the dependence of seeing on wind speed is not significant.

A Computational Model of Cn2 Profile Inversion for Atmospheric Laser Communication in the Vertical Path.

In this paper, an atmospheric structure constant Cn2 model is proposed for evaluating the channel turbulence degree of atmospheric laser communication. First, we derive a mathematical model for the correlation between the atmospheric coherence length r0, the isoplanatic angle θ0 and Cn2 using the Hufnagel-Valley (HV) turbulence model. Then, we calculate the seven parameters of the HV model with the actual measured r0 and θ0 data as input quantities, so as to draw the Cn2 profile and the θ0 profile. The experimental results show that the fitted average Cn2 contours and single-day Cn2 contours have superior fitting performance compared with our historical data, and the daily correlation coefficient between the single-day computed θ0 contours and the measured θ0 contours is up to 87%. This result verifies the feasibility of the proposed method. The results validate the feasibility of the proposed method and provide a new technical tool for the inversion of turbulence Cn2 profiles.

Optical turbulence forecasts at short time-scales using an autoregressive method at the Very Large Telescope

ABSTRACT In this study we demonstrate that we can provide forecasts of all the main astroclimatic parameters (seeing, wavefront coherence time, isoplanatic angle, and ground-layer fraction) on time-scales of 1 and 2 h (the most critical ones for the service mode) with a root-mean-square error (RMSE) that is smaller than or, at worst, comparable to the instrumental uncertainty (i.e. the standard deviation between instrument estimates). The seeing RMSE is 0.08 arcsec. Results are achieved thank to the use of the autoregressive method (AR) in our automatic forecast system and the study is applied to the Very Large Telescope (VLT). The AR method is a hybrid method taking into account forecasts of a non-hydrostatical mesoscale model jointly with real-time observations made in situ. We demonstrate that the AR method allows an improvement in forecast performance of roughly a factor of three or more with respect to the standard forecasts at a long time-scale (beginning of the afternoon for the coming night), depending on the parameter and the time-scale (1 and 2 h). The AR method also allows roughly a factor of two gain with respect to prediction by persistence. We also show that the AR method provides significantly better performance than a random-forest machine-learning algorithm. An extended analysis of the AR performance is provided following different strategies. Results achieved in this study are therefore very promising and tell us that we can provide real assistance to the service mode of the VLT instrumentation supported by adaptive optics systems.