JOURNAL ARTICLE
Reference optical turbulence characteristics at the Large Solar Vacuum Telescope site.
Published In: Publications of the Astronomical Society of Japan, 2024, v. 76, n. 3. P. 538 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Shikhovtsev, Artem Yu 3 of 3
Abstract
The article focuses on a new statistical method to estimate vertical profiles of optical turbulence and wind characteristics relevant to the design of adaptive optics systems for large ground-based solar telescopes, specifically at the Large Solar Vacuum Telescope (LSVT) site in Russia. Using Era-5 reanalysis data combined with long-term turbulence measurements from sonic anemometers, the method refines the structure constant of air refractive index fluctuations (|$C_n^2$|) by minimizing discrepancies between modeled and observed values, accounting for seasonal variations. The study provides representative seasonal profiles of key optical turbulence parameters—including the Fried parameter (r0), isoplanatic angle (θ0), turbulence velocity (V0), and wavefront coherence time (τ0)—which are critical for optimizing adaptive optics performance. Results indicate that optical turbulence is strongest in winter and weakest in summer, with the latter offering the most favorable conditions for high-resolution solar observations. This approach enables continuous, seasonally adjusted characterization of atmospheric turbulence, supporting the development of advanced solar adaptive optics technologies.
Additional Information
- Source:Publications of the Astronomical Society of Japan. 2024/06, Vol. 76, Issue 3, p538
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:0004-6264
- DOI:10.1093/pasj/psae031
- Accession Number:177947907
- Copyright Statement:Copyright of Publications of the Astronomical Society of Japan is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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