The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Application of Neural Networks to Estimation and Prediction of Seeing at the Large Solar Telescope Site
Open accessAbstract
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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“The application of machine learning towards the quality control of observational data is developing but remains niche, for example, in agriculture and astronomy [12,13].”
From Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection (2025) · cites this paper as [13]
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Cites
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3803 · Cited by 31,882
Heuristic self-organization in problems of engineering cybernetics
Ivakhnenko, A. G. (1970) · Automatica · doi:10.1016/0005-1098(70)90092-0 · Cited by 394
The Daniel K. Inouye Solar Telescope – Observatory Overview
Rimmele, T. R., Warner, M., Keil, S. L., et al. (2020) · Solar Physics · doi:10.1007/s11207-020-01736-7 · Cited by 378
From Differential Image Motion to Seeing
Tokovinin, A. A. (2002) · Publications of the Astronomical Society of the Pacific · doi:10.1086/342683 · Cited by 229
3D mapping of optical turbulence using an atmospheric numerical model
Masciadri, E., Vernin, J., & Bougeault, P. (1999) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:1999474 · Cited by 120
Optical turbulence forecast: ready for an operational application
Masciadri, E., Lascaux, F., Turchi, A., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw3111 · Cited by 69
Atmospheric and seeing forecast: WRF model validation with in situ measurements at ORM★
Giordano, C., Vernin, J., Ramió, H. V., et al. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt117 · Cited by 66
Lidar Studies of Wind Turbulence in the Stable Atmospheric Boundary Layer
Banakh, V. A., & Smalikho, I. N. (2018) · Remote Sensing · doi:10.3390/rs10081219 · Cited by 63
Wang, Y., & Basu, S. (2016) · Optics Letters · doi:10.1364/ol.41.002334 · Cited by 62
Modeling Optical Turbulence and Seeing over Mauna Kea*
Cherubini, T., Businger, S., Lyman, R., et al. (2008) · Journal of Applied Meteorology and Climatology · doi:10.1175/2007jamc1487.1 · Cited by 55
Qing, C., Wu, X., Li, X., Zhu, W., Qiao, C., Rao, R., & Mei, H. (2016) · Optics Express · doi:10.1364/oe.24.013303 · Cited by 47
Daytime optical turbulence and wind speed distributions at the Baikal Astrophysical Observatory
Bolbasova, L. A., Shikhovtsev, A. Y., Kopylov, E. A., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2706 · Cited by 42
Self-citationAdaptive optics parameters connection to wind speed at the Teide Observatory
García-Lorenzo, B., Eff-Darwich, A., Fuensalida, J. J., et al. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15071.x · Cited by 38
Filtering techniques to enhance optical turbulence forecast performances at short time-scales
Masciadri, E., Martelloni, G., & Turchi, A. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3342 · Cited by 35
Atmospheric coherent turbulence
Nosov, V. V., Kovadlo, P. G., Lukin, V. P., et al. (2013) · Atmospheric and Oceanic Optics · doi:10.1134/s1024856013030123 · Cited by 33
Self-citationMachine-learning informed macro-meteorological models for the near-maritime environment
Jellen, C., Oakley, M., Nelson, C. L., et al. (2021) · Applied Optics · doi:10.1364/ao.416680 · Cited by 28
Lascaux, F., Masciadri, E., & Fini, L. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt1803 · Cited by 26
Statistics and analysis of high-altitude wind above the Canary Islands observatories
Chueca, S., Garcia-Lorenzo, B. M., Muñoz-Tuñón, C., et al. (2004) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2004.07523.x · Cited by 23
Qian, X., Yao, Y., Wang, H., et al. (2020) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/abc22d · Cited by 20
Bolbasova, L. A., Andrakhanov, A., & Shikhovtsev, A. Y. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab953 · Cited by 19
Self-citationCharacterizing daytime wind profiles with the wide-field Shack–Hartmann wavefront sensor
Wang, Z., Zhang, L., & Rao, C. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3417 · Cited by 17
Project of the Large Solar Telescope with mirror 3 m in diameter
Grigoryev, V. M., Demidov, M. L., Kolobov, D. Y., et al. (2020) · Solar-Terrestrial Physics · doi:10.12737/stp-62202002 · Cited by 17
Formation of Turbulence at Astronomical Observatories in Southern Siberia and North Caucasus
Nosov, V. V., Lukin, V. P., Nosov, E. V., et al. (2019) · Atmospheric and Oceanic Optics · doi:10.1134/s1024856019040110 · Cited by 16
Self-citationMulti-Cascade Image Correction System for the Large Solar Vacuum Telescope
Lukin, V. P., Botygina, N. N., Антошкин, Л. В., et al. (2019) · Atmospheric and Oceanic Optics · doi:10.1134/s1024856019050117 · Cited by 14
Self-citationPrediction of temporal atmospheric boundary layer height using long short-term memory network
Kumar, N., Soni, K., & Agarwal, R. (2021) · Tellus A Dynamic Meteorology and Oceanography · doi:10.1080/16000870.2021.1926132 · Cited by 14
Analysis of the Optical Turbulence Model Using Meteorological Data
Xu, M., Shao, S., Weng, N., et al. (2022) · Remote Sensing · doi:10.3390/rs14133085 · Cited by 14
Optical turbulence in the atmospheric surface layer at the Ali Observatory, Tibet
Qian, X., Yao, Y., Zou, L., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3811 · Cited by 13
Atmospheric Research for Adaptive Optics
Bolbasova, L. A., & Lukin, V. P. (2022) · Atmospheric and Oceanic Optics · doi:10.1134/s1024856022030022 · Cited by 13
Self-citationForecasting surface-layer optical turbulence above the Tibetan Plateau using the WRF model
Yang, Q., Wu, X., Luo, T., et al. (2022) · Optics & Laser Technology · doi:10.1016/j.optlastec.2022.108217 · Cited by 9
Current concept for the 4-m European Solar Telescope (EST) optical design
Sánchez-Capuchino, J., Collados, M., Soltau, D., et al. (2010) · International Optical Design Conference and Optical Fabrication and Testing · doi:10.1364/iodc.2010.itua4p · Cited by 4
Masciadri, E., Bougeault, P., Vernin, J., et al. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.390196 · Cited by 3
Kamlah, R., Verma, M., Diercke, A., et al. (2021) · Solar Physics · doi:10.1007/s11207-021-01771-y · Cited by 3
Zhang, K., Wang, F., Weng, N., et al. (2022) · Remote Sensing · doi:10.3390/rs14164104 · Cited by 3
Smalikho, I. N., Banakh, V. A., & Falits, A. V. (2018) · XXVIII Международный симпозиум «Оптика атмосферы и океана. Физика атмосферы» · doi:10.1117/12.2504481 · Cited by 2
Research works printed but not linked: 2
- Sarazin, 1990, A&A, 227, 294
- Tyryshkin, 2015, Vol. 6, 107
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Sorted by citations
Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection
de Feiter, V. S., Strickland, J. M. I., & Garcia-Marti, I. (2025) · Atmosphere · doi:10.3390/atmos16020129 · Cited by 3
Metadata
- Authors
- Shikhovtsev, A. Y., Kovadlo, P. G., Kiselev, A. V., Eselevich, M. V., & Lukin, V. P.
- Affiliations
- Russian Academy of Sciences (RU)
- Corresponding author
- Shikhovtsev, Artem Yu.
- ORCID
- Venue
- Publications of the Astronomical Society of the Pacific
- Publisher
- IOP Publishing
- Published
- 2023-01-01
- Volume
- 135
- Issue
- 1043
- Pages
- 014503
- ISSN
- 0004-6280 (ISSN-L)
- 1538-3873
- OpenAlex
- W4319316166
- Cited by
- 22
- References
- 34
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 4 · 2023: 5
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Publications of the Astronomical Society of the Pacific (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Adaptive optics and wavefront sensing
- Physical Sciences › Physics and Astronomy › Instrumentation › Astronomy and Astrophysical Research
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astronomical Observations and Instrumentation
- Keywords
- seeing, optical turbulence, differential image motion monitor, multilayer neural networks, group method of data handling, meteorological parameters, wind speed, atmospheric vorticity
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