Boundary-layer atmospheric stability, wind-shear variability, and thermodynamic forcing are all important factors for the initiation, intensification, and transport of dust storms. But there is limited knowledge of the quantitative evaluation of bulk-layer atmospheric stability and the relation between wind-driven dust dynamics and atmospheric stability in arid and semi-arid regions. In this research, a hybrid AI–meteorology framework, HyMet-Fusion, is presented that combines visual information derived from satellite observations with physics-based indicators of atmospheric stability to evaluate atmospheric stability during dust storm events over Iraq. The proposed framework is based on the use of deep features extracted from the satellite imagery through a frozen EfficientNetB0 backbone, combined with indicators derived from the ERA5 pressure level data for the atmosphere, such as the Bulk Richardson Number (Bulk Ri), the Wind Shear (WS) and the Dry Air Index (DAI). The two branches were merged using a late fusion (0.75 physics/0.25 image) and each hour was classified into three atmospheric stability conditions: Relatively Stable, Moderately Unstable and Unstable. The overall hourly accuracy using a Leave-One-Event-Out (LOEO) cross-validation scheme, where each dust event was used for independent testing and no dust event was used for training, was 72.4%, with 81.2% accuracy for the dominant stability state and 92.2% correct assessment of the unstable condition time for the severe dust events. Inaccuracies were mainly (66%) in the conservative direction (more instability). Unstable atmospheric conditions were also found to be associated with all severe dust storms and coincided with higher wind shear, lower Bulk Ri values and higher thermodynamic variability. Moderate and light dust events were primarily associated with transitional and relatively stable atmospheric conditions, and differed between the various regions, primarily in Kirkuk and Nasiriyah. Correlation analysis showed that wind shear had the highest correlation with atmospheric instability (r = 0.92), followed by DAI (r = 0.90) and Bulk Ri (r = −0.75). In addition, the wind shear also increased significantly from light to severe dust events at all stations investigated, showing that wind shear is a critical factor for turbulent mixing, vertical momentum exchange and dust uplift processes. The results suggest wind shear is the leading dynamics mechanism for bulk-layer instability in Iraqi dust storms. The findings highlight the complementary benefit of using physics-based atmospheric indicators embedded with deep learning satellite image analysis. The HyMet-Fusion system can be used as a transferable method for observing wind-driven instability of the atmosphere and related dust hazards, which could be employed in boundary-layer meteorology, air-quality forecasting, aviation safety and environmental risk assessment in arid and semi-arid areas.
Al-Kaissi et al. (2026) studied this question.