  Deep learning-based weather prediction models have advanced significantly in
recent years. However, data-driven models based on deep learning are difficult
to apply to real-world applications because they are vulnerable to
spatial-temporal shifts. A weather prediction task is especially susceptible to
spatial-temporal shifts when the model is overfitted to locality and
seasonality. In this paper, we propose a training strategy to make the weather
prediction model robust to spatial-temporal shifts. We first analyze the effect
of hyperparameters and augmentations of the existing training strategy on the
spatial-temporal shift robustness of the model. Next, we propose an optimal
combination of hyperparameters and augmentation based on the analysis results
and a test-time augmentation. We performed all experiments on the W4C22
Transfer dataset and achieved the 1st performance.
