用物理特征让气象模型跨地区预测大雾，不依赖具体地点。
Geographic Transferability of Machine Learning Models for Short-Term Airport Fog Forecasting
- 用无位置依赖的物理特征训练模型，避免地域特异性。
- 跨1.1万公里多地测试，准确率AUC达0.923至0.947。
- 适合需要通用大气预报工具的研究者和气象机构。
机场短时大雾（能见度<1.0公里）的地理泛化能力是机器学习建模的挑战，因多数模型依赖地点特有特征而难以跨站点迁移。本研究探讨是否可通过坐标无关（位置无关）的热力学与辐射过程特征实现地理可迁移性。以智利圣地亚哥（SCEL，33°S）2002–2009年数据训练的梯度提升分类器（XGBoost），在2010–2012年预留数据集及普埃托蒙特（SCTE）、旧金山（KSFO）、伦敦（EGLL）的严格零样本测试中表现稳定，跨距离达11,650公里，涵盖辐射、平流、海洋等多种大雾类型，AUC值维持在0.923–0.947之间。一致性SHAP特征排序表明，能见度持续性、太阳角度与热力梯度为关键预测因子，说明模型学习的是可迁移的物理关系而非局部模式。结果表明，融合物理先验的坐标无关特征工程可构建具有地理可迁移性的大气预报工具。
原文摘要 · Abstract (English)
Short-term forecasting of airport fog (visibility < 1.0 km) presents challenges in geographic generalization because many machine learning models rely on location-specific features and fail to transfer across sites. This study investigates whether fundamental thermodynamic and radiative processes can be encoded in a coordinate-free (location-independent) feature set to enable geographic transferability. A gradient boosting classifier (XGBoost) trained on Santiago, Chile (SCEL, 33S) data from 2002-2009 was evaluated on a 2010-2012 holdout set and under strict zero-shot tests at Puerto Montt (SCTE), San Francisco (KSFO), and London (EGLL). The model achieved AUC values of 0.923-0.947 across distances up to 11,650 km and different fog regimes (radiative, advective, marine). Consistent SHAP feature rankings show that visibility persistence, solar angle, and thermal gradients dominate predictions, suggesting the model learned transferable physical relationships rather than site-specific patterns. Results suggest that physics-informed, coordinate-free feature engineering can yield geographically transferable atmospheric forecasting tools.
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