AviaSafe：面向航空安全关键云预报的物理信息数据驱动模型
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
将航空结冰指数（IC）作为物理约束嵌入分层预测框架，先以掩码注意力定位稀疏云区、再回归微物理组分浓度，这种「先定位稀疏区域再稠密回归」的两阶段设计可迁移到其他极端稀疏、分布不连续的视觉预测任务（如降水临近预报、烟雾/气溶胶分割）。
原文依据 The model employs a hierarchical architecture that first predicts cloud spatial distribution through masked attention, then quantifies species concentrations within identified regions.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当前的人工智能天气预报模型能够预测常规大气变量，却无法区分对航空安全至关重要的云微物理组分。本文提出 AviaSafe，一种分层、物理信息驱动的神经预报器，可对上述四种水凝物组分给出全球范围、每六小时一次、预报时效长达 7 天的预测。我们的方法针对云预测特有的挑战：极端稀疏性、不连续分布以及组分之间复杂的微物理相互作用。我们将航空气象学中的结冰条件（IC）指数作为基于物理的约束引入，用以识别过冷水驱动冰晶爆发式增长的区域。该模型采用分层架构，首先通过掩码注意力预测云的空间分布，随后在识别出的区域内量化各组分浓度。在 ERA5 再分析数据上训练后，本模型在云组分上取得了比基线更低的 RMSE，并在 7 天预报时效下于若干关键变量上优于业务化数值模型。对单个云组分进行预报的能力，为航空航路优化开辟了新的应用，因为区分冰与水决定了发动机结冰风险。
英文原文摘要 1267 字符
Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introduce AviaSafe, a hierarchical, physics-informed neural forecaster that produces global, six-hourly predictions of these four hydrometeor species for lead times up to 7 days. Our approach addresses the unique challenges of cloud prediction: extreme sparsity, discontinuous distributions, and complex microphysical interactions between species. We integrate the Icing Condition (IC) index from aviation meteorology as a physics-based constraint that identifies regions where supercooled water fuels explosive ice crystal growth. The model employs a hierarchical architecture that first predicts cloud spatial distribution through masked attention, then quantifies species concentrations within identified regions. Training on ERA5 reanalysis data, our model achieves lower RMSE for cloud species compared to baseline and outperforms operational numerical models on certain key variables at 7-day lead times.The ability to forecast individual cloud species enables new applications in aviation route optimization where distinguishing between ice and liquid water determines engine icing risk.