用物理约束的神经网络，精准预测影响飞行安全的云微物理成分。
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
- 分层架构结合掩码注意力，先定位云分布再定量分析成分。
- 7天预报下对四种云微物理物种的均方根误差更低，优于现有数值模型。
- 可区分冰水与液态水，助力航线优化规避结冰风险。
当前人工智能天气预报模型仅能预测常规大气变量，无法区分对航空安全至关重要的云微物理物种。本文提出AviaSafe，一种分层、物理信息驱动的神经气象预报模型，实现全球范围、每六小时一次、最长7天预报期的四种水成物物种预测。该方法应对云预测的独特挑战：极低稀疏性、不连续分布及物种间复杂微物理相互作用。模型引入航空气象学中的结冰条件（IC）指数作为物理约束，识别过冷水导致冰晶暴增的区域。采用分层架构，先通过掩码注意力预测云空间分布，再在确定区域内量化各物种浓度。基于ERA5再分析数据训练，模型在云物种预测上均方根误差低于基准模型，并在7天预报时对某些关键变量超越运行中数值模式。能够分别预测不同云物种，为航路优化提供新可能——区分冰水与液态水可有效评估发动机结冰风险。
原文摘要 · Abstract (English)
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.
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