Python programing Computational Analysis of Bulk-Layer Dynamic and Thermal Stability During Severe Dust Storms in Iraq

Abstract

Dust storms are closely linked to atmospheric instability and its role in initiation, development and persistence of dust storms is especially significant in arid and semi-arid regions like Iraq. Although the dust storm has become more frequent and intense over the years, few quantitative measurements have been made of bulk-layer stability in the atmosphere over different climate regions of Iraq. In this study, a computational framework has been developed in Python, for the analysis of the severity, severity index, and severity analysis of the dust storm during severe, moderate, and light events, using ERA5 reanalysis data from the selected Iraqi meteorological stations, for atmospheric dynamic and thermal instability assessment.
The proposed framework combines cutting-edge atmospheric computation capabilities with automated data processing and visualization functionalities developed with Python scientific libraries, such as MetPy, NumPy, SciPy, Pandas, Matplotlib, and Xarray. Three physically meaningful stability indicators of the atmosphere were computed: Bulk Richardson Number (Ri), Wind Shear (WS) and Dry Air Index (DAI). They were studied at various pressure levels from 1000 to 200 hPa to provide the turbulence intensity, vertical wind interactions and dry air characteristics of dust storm evolution.
The results showed significant differences in the atmospheric stability between the studied stations in both spatial and temporal scales. The Bulk Richardson Number had a tendency to be lower for severe dust storms than for light and moderate dust storms, as did the wind shear, the DAI range and the instability score. The physical instability was highest in Ramadi, Hilla, Samawah, Baghdad and Al-Hayy, indicating the predominant role of dynamic turbulence and dry-air intrusions in the development of dust storms. The interpretation of Ri, WS and DAI has been combined and has successfully captured the atmospheric conditions suitable for dust lifting, transporting and persistence.
The study showed the advantages of using automatic computational meteorology for the diagnosis of the atmospheric instability and also presented a reproducible methodology for the dust storm monitoring, environmental assessment and environmental nanotechnology applications. The results have added to the knowledge of the dynamics of dust storms in Iraq, which can help developing advanced monitoring and early-warning systems for dust storms.