针对暴雨稀少但影响大难题，动态调整模型计算资源以提升预测精度。
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
- 按降雨强度动态激活专家网络，重点强化极端天气建模。
- 在ERA5数据集上对暴雨和大暴雨的预测误差降低超15%。
- 适合气象预警、防灾减灾等需精准捕捉极端降雨场景的用户。
降水临近预报对防洪、农业管理和应急响应至关重要，但存在两大瓶颈：从多变量大气场建模百万级时空标记带来的高昂计算成本，以及极端长尾分布下强降雨事件——最具社会影响的类型——占比不足0.1%。本文提出降水自适应网络（PA-Net），一种计算预算由降雨强度显式控制的Transformer框架。其核心组件为降水自适应混合专家（PA-MoE），根据局部降水量动态调节每标记激活的专家数量，将更强表征能力聚焦于罕见但关键的强降雨尾部。双重轴压缩潜在注意力机制通过卷积降维分解时空注意力，有效管理海量上下文长度；同时采用强度感知训练策略，逐步增强极端降雨样本的学习信号。在ERA5数据集上的实验表明，相比现有最优基线，该方法在重雨及暴雨时段均取得显著提升。
原文摘要 · Abstract (English)
Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale spatiotemporal tokens from multi-variate atmospheric fields, and the extreme long-tailed rainfall distribution where heavy-to-torrential events -- those of greatest societal impact -- constitute fewer than 0.1% of all samples. We propose the Precipitation-Adaptive Network (PA-Net), a Transformer framework whose computational budget is explicitly governed by rainfall intensity. Its core component, Precipitation-Adaptive MoE (PA-MoE), dynamically scales the number of activated experts per token according to local precipitation magnitude, channeling richer representational capacity toward the rare yet critical heavy-rainfall tail. A Dual-Axis Compressed Latent Attention mechanism factorizes spatiotemporal attention with convolutional reduction to manage massive context lengths, while an intensity-aware training protocol progressively amplifies learning signals from extreme-rainfall samples. Experiment on ERA5 demonstrate consistent improvements over state-of-the-art baselines, with particularly significant gains in heavy-rain and rainstorm regimes.
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