| Autor: |
Shikhovtsev, Artem Y., Kiselev, Alexander V., Kovadlo, Pavel G., Kopylov, Evgeniy A., Kirichenko, Kirill E., Ehgamberdiev, Shuhrat A., Tillayev, Yusufjon A. |
| Předmět: |
|
| Zdroj: |
Atmosphere; Jan2024, Vol. 15 Issue 1, p38, 18p |
| Abstrakt: |
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 C n 2 (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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
