通过长短时知识蒸馏提升降水短临预报精度，兼顾准确性与推理效率。
SimCast: Enhancing Precipitation Nowcasting with Short-to-Long Term Knowledge Distillation
- 设计长短时知识蒸馏框架，利用短期预测指导长期预测。
- 在三个数据集上实现0.452~0.474的平均CSI，显著优于现有方法。
- 适合气象预报、灾害预警等需要高精度实时降水预测的场景。
降水短临预报基于当前观测预测未来雷达序列，是受地球系统复杂性驱动的高难度任务。准确预报对防灾减灾、农业、交通和能源优化至关重要。本文研究预测时长对模型的影响，提出SimCast训练框架，采用短至长时知识蒸馏结合加权MSE损失，重点强化强降雨区域的预测。该方法无需增加推理开销即可提升预测质量。由于其生成确定性输出，进一步将其集成至扩散模型框架CasCast，融合概率模型优势，缓解确定性输出的模糊与分布偏移问题。在三个基准数据集上的实验验证了有效性：SEVIR上平均CSI达0.452，HKO-7上为0.474，MeteoNet上为0.361，显著超越现有方法。
原文摘要 · Abstract (English)
Precipitation nowcasting predicts future radar sequences based on current observations, which is a highly challenging task driven by the inherent complexity of the Earth system. Accurate nowcasting is of utmost importance for addressing various societal needs, including disaster management, agriculture, transportation, and energy optimization. As a complementary to existing non-autoregressive nowcasting approaches, we investigate the impact of prediction horizons on nowcasting models and propose SimCast, a novel training pipeline featuring a short-to-long term knowledge distillation technique coupled with a weighted MSE loss to prioritize heavy rainfall regions. Improved nowcasting predictions can be obtained without introducing additional overhead during inference. As SimCast generates deterministic predictions, we further integrate it into a diffusion-based framework named CasCast, leveraging the strengths from probabilistic models to overcome limitations such as blurriness and distribution shift in deterministic outputs. Extensive experimental results on three benchmark datasets validate the effectiveness of the proposed framework, achieving mean CSI scores of 0.452 on SEVIR, 0.474 on HKO-7, and 0.361 on MeteoNet, which outperforms existing approaches by a significant margin.
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