Forecasting surface-layer optical turbulence above the Tibetan Plateau using the WRF model

Yang, Qike; Wu, Xiaoqing; Luo, Tao; Qing, Chun; Yuan, Renmin; Su, Changdong; Xu, Changshun; Wu, Yang; Ma, Xuebin; Wang, Zhiyuan

doi:10.1016/j.optlastec.2022.108217

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…In order to diagnose and predict optical turbulence characteristics, mesoscale models and approaches for describing atmospheric flows with spatial scales of 1000 m or less are being intensively developed (Masciadri et al 1999(Masciadri et al , 2000Cherubini et al 2008;Giordano et al 2013). For example, Qing et al (2016), Qian et al (2020Qian et al ( , 2022, and Yang et al (2022) presented calculations of mesoscale atmospheric characteristics based on the weather research and forecasting (WRF) model. The WRF model is a useful tool for determination and prediction of mesoscale structures in atmospheric flows.…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Networks to Estimation and Prediction of Seeing at the Large Solar Telescope Site

Shikhovtsev¹,

Kovadlo²,

Kiselev³

et al. 2023

PASP

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Optical turbulence limits the angular resolution of ground-based astronomical telescopes. The key parameter of optical turbulence is seeing. In this study, seasonal variations of seeing estimated from differential image motion monitor measurements at the Large Solar Telescope site are discussed. The Large Solar Telescope will be located at an elevation of 2000 m above sea level ( 51 ° 37 ′ 18 ″ N, 100 ° 55 ′ 07 ″ E ). The highest seeing values are observed in winter. The median of seeing is 2.″1. In summer, the median decreases to 1.″1. The best atmospheric conditions are observed in April–May, when the medians of seeing are low and the standard deviations are high. During this period, atmospheric situations with low values of seeing (∼0.″5–0.″6) are often observed. We simulated multilayer neural networks for the measured seeing by applying a group method of data handling. Modeled seeing is well described in terms of mean meteorological parameters, which include wind speed components and large-scale vorticity of air flows at different altitudes in the atmosphere. The 12-layer optimal neural network obtained has a high correlation coefficient between modeled and measured seeing values. The linear correlation coefficient is 0.77.

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Section: Introductionmentioning

confidence: 99%

Application of Neural Networks to Estimation and Prediction of Seeing at the Large Solar Telescope Site

Shikhovtsev¹,

Kovadlo²,

Kiselev³

et al. 2023

PASP

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“…The Weather Research and Forecasting (WRF) model is widely used in simulations of atmospheric movements and pollution [12][13][14][15][16][17]. In recent years, the WRF model has been used to diagnose and predict optical turbulence as well as astronomical image quality characteristics (including seeing) [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Simulating Atmospheric Characteristics and Daytime Astronomical Seeing Using Weather Research and Forecasting Model

Shikhovtsev,

Kovadlo,

Lezhenin

et al. 2023

Applied Sciences

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The present study is aimed at the development of a novel empirical base for application to ground-based astronomical telescopes. A Weather Research and Forecasting (WRF) model is used for description of atmospheric flow structure with a high spatial resolution within the Baikal Astrophysical Observatory (BAO) region. Mesoscale vortex structures are found within the atmospheric boundary layer, which affect the quality of astronomical images. The results of simulations show that upward air motions in the lower atmosphere are suppressed both above the cold surface of Lake Baikal and inside mesoscale eddy structures. A model of the outer scale of turbulence for BAO is developed. In this work, we consider the seeing parameter that represents the full width at half-maximum of the point spread function. Optical turbulence profiles are obtained and daytime variations of seeing are estimated. Vertical profiles of optical turbulence are optimized taking into account data from direct optical observations of solar images.

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“…Atmospheric turbulence [1] is the main form of motion in the atmospheric boundary layer. When a laser beam propagates through the atmosphere, atmospheric turbulence influences it, producing beam broadening and scintillation [2][3][4][5][6]. The atmospheric refractive index structure constant C 2 n is an essential parameter reflecting the influence of turbulence on the light field during the transmission process.…”

Section: Introductionmentioning

confidence: 99%

Study of the Cn2 Model through the New Dimensionless Temperature Structure Function near the Sea Surface in the South China Sea

et al. 2023

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The refractive index structure constant Cn2 near the ocean surface is an important parameter for studying atmospheric optical turbulence over the ocean. The measured refractive index structure constant and meteorological parameters, such as temperature and three-dimensional wind speed, near the sea surface on the South China Sea during the period from January to November 2020 were analyzed. On the basis of Monin–Obukhov similarity theory, the dimensionless temperature structure parameter function fT near the sea surface was established, and a new parameterized model of the near-sea surface was proposed. The new model improved the error of the widely used model proposed by Wyngaard in 1973 (W73) and better reproduced the daily variation in the measured Cn2. Further analysis of the seasonal applicability of the new model indicated that the correlation coefficients between the estimated and measured Cn2 in the spring, summer, autumn, and winter were 0.94, 0.94, 0.95, and 0.89, respectively, and the root mean square errors were 0.32, 0.41, 0.46, and 0.40 m−2/3, respectively. Compared with the Cn2 estimated by the W73 model, the correlation coefficient of Cn2 estimated by the new model and measured by the micro-thermometer increased by 0.05–0.27 and the root mean square error decreased by 0.04–0.56. The improved fT demonstrated higher accuracy than the existing models, which can lay a foundation for estimating turbulence parameters in different sea areas.

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Forecasting surface-layer optical turbulence above the Tibetan Plateau using the WRF model

Cited by 6 publications

References 39 publications

Application of Neural Networks to Estimation and Prediction of Seeing at the Large Solar Telescope Site

Application of Neural Networks to Estimation and Prediction of Seeing at the Large Solar Telescope Site

Simulating Atmospheric Characteristics and Daytime Astronomical Seeing Using Weather Research and Forecasting Model

Study of the Cn2 Model through the New Dimensionless Temperature Structure Function near the Sea Surface in the South China Sea

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