Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data

Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every five minutes, known as Micro data) and hourly atmospheric numerical outputs (termed as Macro data) for fine-resolution weather forecasting. The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons. Each instance of the MiMa model, called a modelet, predicts the values of a specific weather parameter at an individual Mesonet station. The approach is extended with Re-MiMa modelets, which are designed to predict weather variables at ungauged locations by training on multivariate data from a few representative stations in a region, tagged with their elevations. Re-MiMa (short for Regional-MiMa) can provide highly accurate predictions across an entire region, even in areas without observational stations. Experimental results show that MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations, marking a significant advancement in weather forecasting accuracy and applicability.

@article{zhang2025_2412.10450,
  title={ Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data },
  author={ Yihe Zhang and Bryce Turney and Purushottam Sigdel and Xu Yuan and Eric Rappin and Adrian Lago and Sytske Kimball and Li Chen and Paul Darby and Lu Peng and Sercan Aygun and Yazhou Tu and M. Hassan Najafi and Nian-Feng Tzeng },
  journal={arXiv preprint arXiv:2412.10450},
  year={ 2025 }
}