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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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 21
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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