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

Zijian Zhu; Qiusheng Huang; Anboyu Guo; Xiaohui Zhong; Hao Li

cs.LGFeb 25, 2026

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

Authors: Zijian Zhu, Qiusheng Huang, Anboyu Guo, Xiaohui Zhong, Hao Li

Organizations: Artificial Intelligence Innovation and Incubation Institute, Fudan University · Shanghai Academy of Artificial Intelligence for Science · Shanghai Innovation Institute · National Marine Environment Forecasting Center ·

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 & tables

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.

	Cloud Microphysics				Background Variables
Model	CIWC50	CLWC100	CRWC250	CSWC600	U500	V500	Z500	T600	Q600
	( $\times 10^{-8}$ g/kg)	( $\times 10^{-8}$ g/kg)	( $\times 10^{-8}$ g/kg)	( $\times 10^{-5}$ g/kg)	(m/s)	(m/s)	(m 2 /s 2 )	(K)	( $\times 10^{-4}$ g/kg)
Baseline	1.059	1.318	1.010	4.359	2.857	2.903	139.52	0.898	6.908
w/o (MP, IC)	1.012	0.892	0.889	4.346	2.857	2.899	138.95	0.900	6.899
w/o IC	1.053	0.968	0.961	4.341	2.832	2.874	137.23	0.890	6.886
AviaSafe Model (Ours)	0.956	0.875	0.863	4.340	2.826	2.871	135.81	0.889	6.855

Table 1: Ablation study results (average RMSE over the first 5 days of the forecast). Lower is better. Best results are in bold.

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.

Figure 6: Comparison of 72-h forecasts at 500 hPa for CTRL vs CNOP-perturbed initial conditions. Initialization: 2024-01-04 12:00 UTC. The map domain spans 82°E–140°E in longitude and 15°N–50°N in latitude. The red bounding box (105°E–121°E, 21°N–30°N) highlights the target region of interest. Rows (top to bottom): Q, T, CLWC, and CIWC. Columns: control forecast (Control, left), forecast with a CNOP initial perturbation (Perturbed, center), and the Perturbed Control difference (right). Units are as indicated by the color bars.

Figure 7: Spatiotemporal evolution of the control forecast at 500 hPa. This figure displays the 72-hour forecast trajectory starting from the unperturbed initial state. The columns represent forecast lead times from +12h to +72h. The rows, from top to bottom, illustrate: specific humidity (Q500) overlaid with wind vectors, temperature (T500) with geopotential height contours, cloud liquid water content (CLWC500), and cloud ice water content (CIWC500). The red bounding box highlights the target region for verification. This baseline evolution serves as the reference for assessing the impact of initial state perturbations.

Figure 8: Spatiotemporal evolution of the CNOP-perturbed forecast at 500 hPa. This figure shows the forecast results initialized with the CNOP-perturbed state ( $\mathbf{x}_{0}+\delta\mathbf{x}_{0}^{*}$ ), using the same variable layout and time intervals as the control forecast.

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