AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts

TL;DR

AviaSafe is a physics-informed neural network model that predicts critical cloud microphysical species for aviation safety, outperforming existing models in accuracy and enabling better risk assessment for flight operations.

Contribution

The paper introduces AviaSafe, a hierarchical, physics-informed neural network that predicts cloud microphysical species for aviation safety, incorporating a physics-based constraint and addressing microphysical complexities.

Findings

Lower RMSE for cloud species compared to baselines

Outperforms operational models at 7-day lead times

Enables new aviation safety applications

Abstract

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.