A measure-correlate-predict approach for optical turbulence (𝐶2𝑛) using gradient boosting

Conference paper (2024)

Authors

Maximilian Pierzyna Atmospheric Remote Sensing - CEG
Sukanta Basu University at Albany - State University of New York
Research Group
Atmospheric Remote Sensing (CEG) (TU Delft)
More Info
expand_more

Published Date

2024

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Atmospheric Remote Sensing

Abstract

We present a machine learning-based measure-correlate-predict approach that predicts a multi-year time-series of optical turbulence strength (Cn2) with high accuracy (r = 0.78 at 16 locations) based on a single year of in-situ Cn2 measurements and reanalysis data.

Files

Pcaop-2024-pth1e.3.pdf
(pdf | 0.755 Mb)
Unknown license
warning

File under embargo until 15-04-2025