用ConvGRU模型高效预测短时降雨，2025天气挑战赛第二名。
Computationally-efficient deep learning models for nowcasting of precipitation: A solution for the Weather4cast 2025 challenge
- 基于ConvGRU的迁移学习框架，输入四次红外卫星数据。
- 两阶段训练：先预测亮温，再转为降水率，可预报4小时降雨。
- 无需调参即可用于事件预测，适合实时气象应用。
本研究提出一种基于卷积门控循环单元（ConvGRU）的迁移学习框架，用于应对Weather4Cast 2025竞赛中的短时降水预报任务。输入为四次一小时间隔的SEVIRI红外波段（10.8 μm）观测数据。采用两阶段训练策略：第一阶段，用ConvGRU预测亮度温度，捕捉时空变化模式；第二阶段，通过经验非线性变换将预测结果映射为OPERA兼容的降水速率。在累积降雨任务中，该方法取得第二名成绩。同一模型未经调整直接应用于事件预测任务，性能与竞赛基准模型相当。
原文摘要 · Abstract (English)
This study presents a transfer-learning framework based on Convolutional Gated Recurrent Units (ConvGRU) for short-term rainfall prediction in the Weather4Cast 2025 competition. A single SEVIRI infrared channel (10.8 μm wavelength) is used as input, which consists of four observations over a one-hour period. A two-stage training strategy is applied to generate rainfall estimates up to four hours ahead. In the first stage, ConvGRU is trained to forecast the brightness temperatures from SEVIRI, enabling the model to capture relevant spatiotemporal patterns. In the second stage, an empirically derived nonlinear transformation maps the predicted fields to OPERA-compatible rainfall rates. For the event-prediction task, the transformed rainfall forecasts are processed using 3D event detection followed by spatiotemporal feature extraction to identify and characterize precipitation events. Our submission achieved 2nd place in the cumulative rainfall task. Further, the same model was used out-of-the-box for the event prediction task, and resulted in similar scores as the baseline model to the competition.
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