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

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

Conference Paper (2024)

Author(s)

Maximilian Pierzyna (TU Delft - Atmospheric Remote Sensing)

Sukanta Basu (State University of New York at Albany)

Rudolf Saathof (TU Delft - Space Systems Egineering)

DOI related publication

https://doi.org/10.1364/PCAOP.2024.PTh1E.3 Final published version

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https://resolver.tudelft.nl/uuid:21e3ffc3-c1d1-40fd-97b5-c453c6a0dfe1

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Publication Year

2024

Language

English

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.

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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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