STAMINA (Spatial-Temporal Aligned Meteorological INformation Attention) and FPL (Focal Precip Loss): Advancements in Precipitation Nowcasting for Heavy Rainfall Events

Precipitation nowcasting is crucial for weather-dependent decision-making in various sectors, providing accurate and high-resolution predictions of precipitation within a typical two-hour timeframe. Deep learning techniques have shown promise in improving nowcasting accuracy by leveraging large radar datasets. However, accurately predicting heavy rainfall events remains challenging due to several persistent problems in previous work. These include spatial-temporal misalignment between meteorological information and precipitation data, as well as the performance gap between different rainfall levels. To address these challenges, we propose two innovative modules: Spatial-Temporal Aligned Meteorological INformation Attention (STAMINA) and Focal Precip Loss (FPL). STAMINA integrates meteorological information using spatial-temporal embedding and pixelwise linear attention mechanisms to overcome spatial-temporal misalignment. FPL addresses event imbalance through event weighting and a penalty mechanism. Through extensive experiments, we demonstrate significant performance improvements achieved by STAMINA and FPL, with an 8% improvement in predicting light rainfall and, more significantly, a 30% improvement in heavy rainfall compared to the state-of-the-art DGMR model. These modules offer practical and effective solutions for enhancing nowcasting accuracy, with a specific focus on improving predictions for heavy rainfall events. By tackling the persistent problems in previous work, our proposed approach represents a significant advancement in the field of precipitation nowcasting.