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A measure-correlate-predict approach for optical turbulence (𝐢2𝑛) using gradient boosting

Conference Paper (2024)
Author(s)

Maximilian Pierzyna (TU Delft - Atmospheric Remote Sensing)

Sukanta Basu (University at Albany - State University of New York)

R Saathof (TU Delft - Space Systems Egineering)

Research Group
Atmospheric Remote Sensing
DOI related publication
https://doi.org/10.1364/PCAOP.2024.PTh1E.3
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Publication Year
2024
Language
English
Research Group
Atmospheric Remote Sensing
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
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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.

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