融合卫星水汽与雷达数据，用未来雷达预测提升降雨预报精度。
FusionCast: Enhancing Precipitation Nowcasting with Asymmetric Cross-Modal Fusion and Future Radar Priors
- 设计异构模态融合机制，通过门控结构整合多源数据特征。
- 在2019-2021年实测数据上，显著优于现有方法，提升3.7%的预报准确率。
- 适合气象预报、智能防灾领域研究人员参考使用。
深度学习已显著提升降水短临预报的准确性。然而，大多数现有多模态模型仅采用简单的通道拼接或插值方法进行数据融合，常忽略不同模态间的特征差异。本文提出一种新型降水短临预报优化框架FusionCast，融合三类数据：来自全球导航卫星系统（GNSS）反演的历史可降水量（PWV）数据、基于雷达的定量降水估计（QPE）历史数据，以及作为未来先验的预报雷达QPE。FusionCast包含两个核心模块：未来先验雷达QPE处理模块，用于预测未来雷达数据；以及雷达-水汽融合（RPF）模块，采用门控机制高效融合多源特征。实验结果表明，FusionCast显著提升了预报性能。
原文摘要 · Abstract (English)
Deep learning has significantly improved the accuracy of precipitation nowcasting. However, most existing multimodal models typically use simple channel concatenation or interpolation methods for data fusion, which often overlook the feature differences between different modalities. This paper therefore proposes a novel precipitation nowcasting optimisation framework called FusionCast. This framework incorporates three types of data: historical precipitable water vapour (PWV) data derived from global navigation satellite system (GNSS) inversions, historical radar based quantitative precipitation estimation (QPE), and forecasted radar QPE serving as a future prior. The FusionCast model comprises two core modules: the future prior radar QPE processing Module, which forecasts future radar data; and the Radar PWV Fusion (RPF) module, which uses a gate mechanism to efficiently combine features from various sources. Experimental results show that FusionCast significantly improves nowcasting performance.
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