SIGMAformer: A Spatiotemporal Gaussian Mixture Correlation Transformer for Global Weather Forecasting

Proposed SIGMAformer framework

The proposed framework replaces the conventional attention mechanism with the DSTC module, which enhances spatiotemporal weather forecasting by leveraging GMM-based pattern extraction to compute and aggregate weighted temporal and spatial correlations.

Usage

1. Global weather datasets can be obtained from [Google Drive].

2. Install Pytorch and other necessary dependencies.

pip install -r requirements.txt

3. Train and evaluate model. We provide the experiment scripts under the folder ./scripts/. You can reproduce the experiment results as the following examples:

bash ./scripts/Global_Temp/SIGMAformer.sh
bash ./scripts/Global_Wind/SIGMAformer.sh