Hourly Temporal Imputation of Five Key Meteorological Variables at 17 Iranian Airport Stations Using a Machine Learning Framework

Meteorological observations are fundamental to environmental modeling, yet data gaps frequently compromise their utility. This study benchmarked five machine-learning algorithms (Random Forest, Extra Trees, Gradient Boosting, Histogram-based Gradient Boosting, and Ridge regression) for hourly temporal imputation of five key meteorological variables—temperature, humidity, dew point, atmospheric pressure, and wind speed—across 17 airport-based synoptic stations in Iran. A comprehensive feature-engineering approach generated 43 predictors capturing calendar, cyclical, continuous composite, binary regime, and cross-variable patterns. Performance was assessed using R2, RMSE, and MAE with time-series cross-validation, bootstrap resampling, split and Mondrian conformal prediction, three robustness stress tests, and comparison against persistence and climatological baselines. Results revealed a striking performance dichotomy. Thermodynamically coupled variables achieved exceptional accuracy, with mean cross-validated R2 values of 0.994 ± 0.003 (temperature), 0.997 ± 0.001 (humidity), and 0.988 ± 0.009 (dew point), with correspondingly low RMSEs (0.74 ± 0.22 °C, 1.10 ± 0.21%, and 0.60 ± 0.19 °C). Feature-ablation confirmed that this skill was driven primarily by inter-variable coupling, with the four cross-variable predictors accounting for 96.3–100% of importance; removing them caused the dew point R2 to collapse to 0.375. In contrast, dynamically controlled variables exhibited substantially lower skill: atmospheric pressure achieved a mean R2 of 0.44 ± 0.25 (RMSE 3.87 ± 0.86 hPa), while wind speed performed poorest (mean R2 0.13 ± 0.22, RMSE 2.20 ± 0.50 m/s), with negative R2 at several stations and no significant improvement over persistence. Their diffuse feature importance profiles and wider conformal prediction intervals indicated that temporal and cross-variable features alone cannot capture advective and synoptic-scale influences. Pressure proved less reconstructable than the thermodynamically coupled variables, reflecting its dependence on large-scale atmospheric dynamics rather than on local site conditions. This study demonstrates that ensemble tree-based models excel at reconstructing variables with strong inter-variable coupling but require spatial and physics-informed integration for dynamic parameters in regions with complex synoptic-scale dynamics.