用气象物理原理增强轻量模型，精准预测机场低能见度。
Physics-Informed Lightweight Machine Learning for Aviation Visibility Nowcasting Across Multiple Climatic Regimes
- 基于热力学原理设计特征，用观测数据训练轻量梯度提升模型。
- 3小时预报召回率提升2.5至4倍，误报显著减少。
- 自动还原局部物理过程，适合航空运行决策与边缘计算部署。
短时低能见度和降水事件的预测对航空安全与运行效率至关重要。现有方法依赖计算量大的数值天气预报和人工发布的航站预报（TAF），常存在保守偏差且时间分辨率有限。本研究提出一种轻量级梯度提升框架（XGBoost），仅使用地面观测数据（METAR）训练，并通过基于热力学原理的物理引导特征工程进行增强。模型在11个代表不同气候区的国际机场（包括SCEL、KJFK、KORD、KDEN、SBGR、VIDP）上评估，覆盖2000至2024年历史数据。结果表明，模型无需人工调参即可捕捉本地物理过程。在盲测对比中，相比操作性TAF预报，该模型在战术时间尺度（3小时）实现更高的检测率，召回率提升2.5至4.0倍，同时降低误报率。此外，SHAP分析显示模型隐式重构了局地物理驱动因素（平流、辐射、下沉），为运行态势感知提供可解释性支持。
原文摘要 · Abstract (English)
Short-term prediction (nowcasting) of low-visibility and precipitation events is critical for aviation safety and operational efficiency. Current operational approaches rely on computationally intensive numerical weather prediction guidance and human-issued TAF products, which often exhibit conservative biases and limited temporal resolution. This study presents a lightweight gradient boosting framework (XGBoost) trained exclusively on surface observation data (METAR) and enhanced through physics-guided feature engineering based on thermodynamic principles. The framework is evaluated across 11 international airports representing distinct climatic regimes (including SCEL, KJFK, KORD, KDEN, SBGR, and VIDP) using historical data from 2000 to 2024. Results suggest that the model successfully captures underlying local physical processes without manual configuration. In a blind comparative evaluation against operational TAF forecasts, the automated model achieved substantially higher detection rates at tactical horizons (3 hours), with a 2.5 to 4.0 times improvement in recall while reducing false alarms. Furthermore, SHAP analysis reveals that the model performs an implicit reconstruction of local physical drivers (advection, radiation, and subsidence), providing actionable explainability for operational situational awareness. Keywords: aviation meteorology; physics-guided machine learning; explainable artificial intelligence; lightweight machine learning; nowcasting; METAR; TAF verification; edge computing
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