OTCliM: generating a near-surface climatology of optical turbulence strength ($C_n^2$) using gradient boosting

TL;DR

OTCliM employs gradient boosting to efficiently generate accurate, multi-year climatologies of atmospheric optical turbulence strength ($C_n^2$), aiding ground-based astronomy and optical communication.

Contribution

This paper introduces OTCliM, a machine learning approach that extrapolates one year's $C_n^2$ measurements into multi-year climatologies, improving site-specific turbulence modeling.

Findings

OTCliM accurately predicts $C_n^2$ for multiple years and diverse sites.

The model outperforms traditional analytical models in prediction accuracy.

Geographical generalization is better in non-urban environments.

Abstract

This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM addresses the challenge of efficiently obtaining reliable site-specific $C_n^2$ climatologies near the surface, crucial for ground-based astronomy and free-space optical communication. Using gradient boosting machines and global reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a multi-year time series. We assess OTCliM's performance using $C_n^2$ data from 17 diverse stations in New York State, evaluating temporal extrapolation capabilities and geographical generalization. Our results demonstrate accurate predictions of four held-out years of $C_n^2$ across various sites, including complex urban environments, outperforming traditional analytical models. Non-urban models also show good geographical generalization compared to urban models, which capture non-general site-specific dependencies. A feature importance analysis confirms the physical consistency of the trained models. It also indicates the potential to uncover new insights into the physical processes governing $C_n^2$ from data. OTCliM's ability to derive reliable $C_n^2$ climatologies from just one year of observations can potentially reduce resources required for future site surveys or enable studies for additional sites with the same resources.