Sequence to sequence architecture based on hybrid LSTM global and local encoders approach for meteorological factors forecasting

Sequence to sequence architecture based on hybrid LSTM global and local encoders approach for meteorological factors forecasting

Abstract Accurate prediction of meteorological factors is critical across various domains such as agriculture, disaster management, and climate research. Traditional models, such as Numerical Weather Prediction (NWP), often face limitations in capturing highly non-linear and chaotic weather patterns...

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Bibliographic Details
Main Authors: Guoqiang Sun, Yang Zhao, Xiaoyan Qi
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Meteorological forecasting
Deep learning
Sequence to sequence
Long short-term memory
Hybrid global-local encoder
Online Access:https://doi.org/10.1038/s41598-025-08331-5
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