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Machine learning for reconstructing meteorological data using ERA5
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DOI：10.1016/j.rineng.2026.111512.png)
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Abstract
En 中文
• This study reconstructs 1950–2025 data for 464 Iranian stations, filling gaps. • Extra Trees model trained on ERA5 reconstructed 9 meteorological variables. • Temperature, dew point and pressure showed high accuracy. • Precipitation and visibility were challenging: RMSEs ∼871 mm and 9741 m. • Datasets enabled climate analysis and planning; local bias correction is advised.
Keywords:
Climate
Artificial intelligence
Meteorology
Weather
Modeling
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