Multi-Scale Spatial-Temporal Transformer for Meteorological Variable Forecasting

Frequent occurrences of marine extreme climate and weather events pose significant threats to human life and property, underscoring the practical significance of meteorological data forecasting methods. Notably, significant advancements in meteorological forecasting fields have been achieved by data-driven deep learning techniques, which leverage observed meteorological datasets and employ deep networks to capture complex patterns. However, challenges remain in accurately extracting local details and capturing spatial-temporal correlations when dealing with multiple meteorological forecasting tasks that exhibits diverse temporal and spatial scales. Hence, in this paper, we propose a Multi-Scale Spatial Temporal Transformer (MS-STT) framework to achieve efficient and accurate meteorological data forecasting. Specifically, to achieve more detailed and multi-scale representation of meteorological data, we design the regionally coherent encoding strategy and multi-scale feature aggregation for visual representation. To enhance the multi-scale ability in terms of learning spatial-temporal correlations, we propose a multi-scale spatial-temporal transformer network, which integrates a multi-scale spatial transformer to learn the spatial association between local patches and multi-scale regions and a temporal transformer to learn the temporal dynamic evolution properties. Extensive quantitative and qualitative experiments on three popular spatial temporal forecasting tasks validate the effectiveness of the proposed method. In particular, compared to the representative data-driven deep learning ENSO forecasting method Earthformer, our approach achieves a 3.7% performance improvement with only one-third of the parameters.