OTCliM：使用梯度提升生成光学湍流强度（$C_n^2$）的近表面气候学
OTCliM: generating a near-surface climatology of optical turbulence strength ($C_n^2$) using gradient boosting
AI总结：
本研究提出OTCliM方法，利用梯度提升机和全球再分析数据将一年测量数据外推为多年时间序列，高效生成近表面光学湍流强度气候学，在17个站点测试中优于传统模型并具备良好地理泛化能力。
摘要：
本研究引入了OTCliM（使用机器学习的光学湍流气候学），这是一种利用梯度提升机推导大气光学湍流强度（$C_n^2$）综合气候学的新方法。OTCliM解决了高效获取近表面特定地点可靠$C_n^2$气候学的挑战，这对于地面天文学和自由空间光通信至关重要。利用梯度提升机和全球再分析数据，OTCliM将一年的$C_n^2$测量数据外推为多年时间序列。我们使用来自纽约州17个不同站点的$C_n^2$数据评估了OTCliM的性能，评估了时间外推能力和地理泛化能力。结果表明，该模型能够准确预测各个站点（包括复杂的城市环境）中留出的四年$C_n^2$数据，优于传统的分析模型。与捕捉了非通用站点特定依赖关系的城市模型相比，非城市模型也展现出良好的地理泛化能力。特征重要性分析证实了训练模型的物理一致性，并表明从数据中揭示控制$C_n^2$物理过程新见解的潜力。OTCliM仅凭一年观测即可推导可靠$C_n^2$气候学的能力，有望减少未来站点勘测所需资源，或使相同资源下能研究更多站点。
英文摘要：
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.
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