VarteX: Enhancing Weather Forecast through Distributed Variable Representation

TL;DR

VarteX introduces a novel variable aggregation and regional training framework that improves weather forecast accuracy while reducing computational resources, advancing deep learning applications in meteorology.

Contribution

The paper presents a new variable aggregation scheme and regional split training method that enhance deep learning-based weather forecasting efficiency and accuracy.

Findings

VarteX outperforms conventional models in forecast accuracy.

Requires fewer parameters and computational resources.

Effective learning through multiple aggregations and regional training.

Abstract

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.