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

TL;DR

AviaSafe is introduced, a hierarchical, physics-informed neural forecaster that produces global, six-hourly predictions of these four hydrometeor species for lead times up to 7 days, addressing the unique challenges of cloud prediction: extreme sparsity, discontinuous distributions, and complex microphysical interactions between species.

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.

Figures (8)

• Figure 1: Architecture of our forecasting framework. The model takes historical atmospheric states $\mathrm{X}_{t-1}$ and $\mathrm{X}_t$ as input to predict $\mathrm{X}_{t+1}$. (a) The overall architecture comprises two modules: 1) The Prediction Backbone (red) with Encoder $\mathbf{E}$, Swin Transformer blocks, and decoupled Decoders ($\mathbf{D_{Cloud}}$, $\mathbf{D}$), outputting feature $\mathbf{F_{backbone}}$ and the final forecast. 2) The Physics-Informed Guidance module (green) with IC Block, Encoder $\mathbf{E_{Mask}}$, Mask Predictor, and a convolutional layer (Conv). It uses physical masks and $\mathbf{F_{backbone}}$ to produce the guidance signal ($\mathrm{Mask}_{t+1}$) for cloud prediction. (b) IC Block: Illustrates the IC Function with temperature ($T$) and humidity ($Q$) inputs. (c) Mask Predictor: Details the internal structure including MLP, Swin Block, and Deconvolutional Decoder (Deconv).
• Figure 2: Performance comparison between our model (AviaSafe) and ECMWF HRES model. Results are averaged over the 00 UTC and 12 UTC initialization times on the test set. The top row shows the latitude-weighted Root Mean Square Error (RMSE, lower is better), and the bottom row shows the latitude-weighted Anomaly Correlation Coefficient (ACC, higher is better) for five key variables at 500hPa. The x-axis represents the forecast lead time up to 7 days (28 steps). In all panels, the red line indicates our proposed AviaSafe model, while the blue line represents the ECMWF HRES model for comparison.
• Figure 3: Mean NRMSE improvement of AviaSafe over the Baseline across all variables for a 7-day forecast. Each row represents different variable, averaged over all 13 pressure levels. Blue indicates that our model is better (lower RMSE). The data shows that AviaSafe outperforms the baseline in 93.7% of all variable/time-step combinations shown.
• Figure 4: Time series of the mean NRMSE improvement for CIWC over a 15-day forecast. The line shows the NRMSE improvement (%) for the CIWC variable, averaged over all pressure levels, where negative values indicate an improvement.
• Figure 5: CNOP-derived spatial importance map for the moist energy norm. Shading shows the CNOP initial-perturbation pattern; black contours denote the initial 500 hPa geopotential height; the red box marks the target region.