VarteX: Enhancing Weather Forecast through Distributed Variable Representation
This work addresses a domain-specific problem for weather forecasting by improving efficiency and accuracy, though it appears incremental as it builds on existing data-driven models.
The study tackled the challenge of efficiently handling multiple meteorological variables in deep learning-based weather forecasting by proposing a new variable aggregation scheme and an efficient learning framework, resulting in VarteX outperforming conventional models with significantly fewer parameters and resources.
Weather forecasting is essential for various human activities. Recent data-driven models have outperformed numerical weather prediction by utilizing deep learning in forecasting performance. However, challenges remain in efficiently handling multiple meteorological variables. This study proposes a new variable aggregation scheme and an efficient learning framework for that challenge. Experiments show that VarteX outperforms the conventional model in forecast performance, requiring significantly fewer parameters and resources. The effectiveness of learning through multiple aggregations and regional split training is demonstrated, enabling more efficient and accurate deep learning-based weather forecasting.