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 article presents a novel machine learning (ML) model, ca...

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Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 21
Main Authors: Zhang, Yihe, Turney, Bryce, Sigdel, Purushottam, Yuan, Xu, Rappin, Eric, Lago, Adrian L., Kimball, Sytske, Chen, Li, Darby, Paul, Peng, Lu, Aygun, Sercan, Tu, Yazhou, Hassan Najafi, M., Tzeng, Nian-Feng
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Atmospheric modeling
Atmospheric numerical values
Data models
encoder-decoder transformer
Encoders-Decoders
Forecasting
Learning algorithms
Long short term memory
Machine learning
machine learning (ML)
Mathematical analysis
Mathematical models
Meteorology
Multivariate analysis
Numerical models
observational data
Observational learning
Predictions
Predictive models
transfer learning
Weather
Weather forecasting
weather prediction
Wind speed
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary: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 article presents a novel machine learning (ML) model, called Micro-Macro (MiMa), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every 5 min, 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 Regional MiMa (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 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.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3541545