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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer.

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Summary

Accurate weather prediction is crucial for daily activities and climate monitoring. A new spatiotemporal coupled prediction network (STWPM) enhances accuracy by capturing complex spatial and temporal weather patterns.

Keywords:
convolutional neural networksdigital earthspatiotemporal series predictiontransformerweather prediction

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Area of Science:

  • Meteorology and Climate Science
  • Artificial Intelligence
  • Geophysics

Background:

  • Accurate weather prediction is vital for human activities, climate monitoring, and environmental protection.
  • Existing data-driven methods struggle to capture complex spatiotemporal dynamics and variable interactions, limiting efficiency and accuracy.
  • Weather phenomena exhibit strong spatial-temporal correlations and inter-variable dependencies, necessitating advanced modeling approaches.

Purpose of the Study:

  • To develop a novel spatiotemporal coupled prediction network (STWPM) for improved multivariate weather forecasting.
  • To address limitations in existing methods regarding the capture of spatial and temporal evolution characteristics.
  • To enhance the accuracy and efficiency of weather prediction for practical applications.

Main Methods:

  • Designed a spatiotemporal coupled prediction network integrating convolutional neural networks and Transformer architecture.
  • Employed a spatial attention encoder-decoder to extract and reconstruct spatial features.
  • Utilized a multi-scale spatiotemporal evolution module for analyzing inter- and intra-frame weather patterns.
  • Implemented a composite loss function (MSE and SSIM) to optimize global and structural weather distribution prediction.

Main Results:

  • The proposed STWPM demonstrated superior performance in multivariate spatiotemporal field weather prediction.
  • Comprehensive evaluations on the ERA5 dataset showed excellent results compared to classical algorithms.
  • The model effectively captures complex spatial and temporal correlations and variable interactions.

Conclusions:

  • The developed spatiotemporal coupled prediction network (STWPM) offers a significant advancement in weather forecasting accuracy.
  • The integration of spatial attention and multi-scale spatiotemporal modules effectively models complex weather dynamics.
  • The findings highlight the potential of deep learning approaches for precise and efficient multivariate weather prediction.