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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Research works printed but not linked: 2
- Sarazin, 1990, A&A, 227, 294
- Tyryshkin, 2015, Vol. 6, 107
Cited by
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
