Statistical learning as a new approach for optical turbulence forecasting

C. Giordano; A. Rafalimanana; A. Ziad; E. Aristidi; J. Chabé; Y. Fanteï-Caujolle; C. Renaud

doi:10.1117/12.2562316

13 December 2020 Statistical learning as a new approach for optical turbulence forecasting

C. Giordano, A. Rafalimanana, A. Ziad, E. Aristidi, J. Chabé, Y. Fanteï-Caujolle, C. Renaud

Proceedings Volume 11448, Adaptive Optics Systems VII; 114484E (2020) https://doi.org/10.1117/12.2562316
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

For the new generation of Extremely Large Telescopes the knowledge of atmospheric turbulence conditions is become an information of primary importance to design and optimize all focal instrumentation. In the same way, the forecast of these atmospheric conditions is also of interest to allow both flexible scheduling and long term site testing. Until now we have used weather forecast tools coupled with turbulence models to predict turbulence conditions. In addition, we are developing a predictive statistical learning tool, using a large atmospheric database. Since 2015, the Calern Observatory hosts the Calern Atmospheric Turbulence Station (CATS) which measures during daytime and nighttime, ground meteorological conditions, vertical profiles of the C²_n and all relevant integrated parameters characterizing the optical turbulence. This large CATS database is used as input for our predictive statistical learning tool. This latter should take into account more closely the local specificities, seasonal variations and day/night transitions. The results from these turbulence predictive models and statistical learning tools are presented and discussed.

Citation Download Citation

C. Giordano, A. Rafalimanana, A. Ziad, E. Aristidi, J. Chabé, Y. Fanteï-Caujolle, and C. Renaud "Statistical learning as a new approach for optical turbulence forecasting", Proc. SPIE 11448, Adaptive Optics Systems VII, 114484E (13 December 2020); https://doi.org/10.1117/12.2562316

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available