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

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, particularly at finer temporal and spatial scales, and they require substantial computational resources. This study introduces a deep learning model, the Hybrid LSTM Global-Local Encoder (H-LSTM-GLE), designed to enhance predictive accuracy in meteorological factors prediction. The H-LSTM-GLE model leverages a local encoder with a a sliding window mechanism, a global encoder for secondary encoding, and a state vector calculation module to improve forecasting precision. When benchmarked against ten baseline models across two datasets, relative humidity (SML2010-Hum) and outdoor temperature (SML2010-outTem), the H-LSTM-GLE model consistently outperforms its conterparts. Ablation studies further validate the model's enhanced performance, attributing the improvements to the synergistic integration of both local and global encoders. This study advances the theoretical framework of sequence to sequence models and offers practical implications of hybrid architectures in achieving high-accuarcy meteorological factors forecasts.