用分层图网络提升天气预报精度，同时大幅降低训练成本。
Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Training
- 设计分层图网络，保留全局趋势并融合物理方程解场。
- 13天预报误差降低超5%，极端天气预测更可靠。
- 仅需1个训练轮次收敛，适合资源受限的研究者。
气候事件由全球尺度驱动的复杂多变量动态过程引发，深刻影响粮食、能源与基础设施。然而，由于物理过程跨越多时空尺度，固定分辨率方法难以捕捉，导致精准天气预测仍具挑战。分层图神经网络（HGNN）提供多尺度表征，但非线性下采样常抹除全局趋势，削弱物理信息融合。本文提出HiFlowCast及其集成版本HiAntFlow，将物理知识嵌入多尺度预测框架。核心创新包括：潜空间记忆保持机制，确保下采样过程中全局趋势不丢失；潜空间到物理分支，跨尺度整合偏微分方程解场。实验显示，其模型在13天预报时长下误差降低超过5%，在第一和第99百分位极端事件中降幅达5%-8%，显著提升罕见事件预测可靠性。借助预训练权重，模型可在单个训练轮次内完成收敛，大幅降低训练成本与碳足迹。该效率对可持续机器学习研究至关重要。代码与模型权重见补充材料。
原文摘要 · Abstract (English)
Climate events arise from intricate, multivariate dynamics governed by global-scale drivers, profoundly impacting food, energy, and infrastructure. Yet, accurate weather prediction remains elusive due to physical processes unfolding across diverse spatio-temporal scales, which fixed-resolution methods cannot capture. Hierarchical Graph Neural Networks (HGNNs) offer a multiscale representation, but nonlinear downward mappings often erase global trends, weakening the integration of physics into forecasts. We introduce HiFlowCast and its ensemble variant HiAntFlow, HGNNs that embed physics within a multiscale prediction framework. Two innovations underpin their design: a Latent-Memory-Retention mechanism that preserves global trends during downward traversal, and a Latent-to-Physics branch that integrates PDE solution fields across diverse scales. Our Flow models cut errors by over 5% at 13-day lead times and by 5-8% under 1st and 99th quantile extremes, improving reliability for rare events. Leveraging pretrained model weights, they converge within a single epoch, reducing training cost and their carbon footprint. Such efficiency is vital as the growing scale of machine learning challenges sustainability and limits research accessibility. Code and model weights are in the supplementary materials.
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