Dust storm hazards

Middleton, Nick

doi:10.1051/e3sconf/20199904001

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 5 shows the HYSPLIT model's backward trajectory output with the Lagrangian approach and the GDAS input data to determine the air mass trajectory and path of particles to the Ahvaz. Ahvaz city, located in the center of Khuzestan province, was targeted because it is surrounded by dust sources, has the highest visibility reduction in two dust cases, and is one of the dustiest cities in the world [70][71][72]. For this purpose, the horizontal tracking of the movement of dust particles for 06:00, 09:00, and 12:00 UTC with a time step of 3 h was carried out.…”

Section: Hysplit Model Simulationsmentioning

confidence: 99%

Investigating the Role of the Low-Level Jet in Two Winters Severe Dust Rising in Southwest Iran

Parno,

Meshkatee,

Mobarak Hassan

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The dust storms with local and non-local dust sources mostly affect Khuzestan province in southwest (SW) Iran. In this study, the role of the low-level jet in the activation of the internal dust events in SW Iran during two severe dust cases was investigated. For this purpose, the fifth-generation ECMWF reanalysis for the global climate and weather (ERA5) data was used to identify the synoptic patterns and the low-level jet (LLJ) characteristics in the study area. Furthermore, the images of the moderate resolution imaging spectroradiometer (MODIS) sensor, the outputs of the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model, and a weather research and forecasting model coupled with chemistry (WRF-Chem) were used to investigate the propagation and transport of the dust particles. The results of the synoptic analysis in both dust cases show the simultaneous occurrence of the divergence zone associated with cyclonic curvature in the subtropical jet stream (STJ) at 300 hPa, causing convergence at 925 hPa, upward motion, and the development of low surface pressure in SW Iran. Examining the vertical wind profile shows the existence of the maximum horizontal wind speeds of 975 to 875 hPa, along with the positive and negative shear below and above it, respectively, which emphasizes the existence of the LLJ and its role in local dust emission. The results of the comparison between the satellite images, WRF-Chem, and HYSPLIT model outputs show the formation and transportation of dust particles from the inner regions of Khuzestan in SW Iran. The horizontal dust surface distribution, vertical raised dust mass, and kinetic energy transfers are well simulated by the WRF-Chem model when LLJ broke at 09:00 to 12:00 UTC. The most important finding of this research is that, for the first time, the role of low-level jet is investigated in the activation of internal dust events in SW Iran.

show abstract

Section: Hysplit Model Simulationsmentioning

confidence: 99%

Investigating the Role of the Low-Level Jet in Two Winters Severe Dust Rising in Southwest Iran

Parno,

Meshkatee,

Mobarak Hassan

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

“…The findings of several studies in the context of Iranian cities also show that the increase in dust storms in recent years has had a significant effect on respiratory mortality and morbidity [12]. In the urban and peri-urban agriculture sectors, except for a few positive impacts of dust storms, they have negatively impacted soil productivity and crop production and, consequently, revenue [13,14]. Therefore, spatiotemporal monitoring, modeling, and forecasting dust-storm phenomena and developing early warning systems can play an important role in mitigating and preventing their environmental, health, and socioeconomic consequences in human settlements, particularly in urban areas.…”

Section: Introductionmentioning

confidence: 98%

Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas

Yarmohamadi,

Alesheikh,

Sharif

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Dust storms are natural disasters that have a serious impact on various aspects of human life and physical infrastructure, particularly in urban areas causing health risks, reducing visibility, impairing the transportation sector, and interfering with communication systems. The ability to predict the movement patterns of dust storms is crucial for effective disaster prevention and management. By understanding how these phenomena travel, it is possible to identify the areas that are most at risk and take appropriate measures to mitigate their impact on urban environments. Deep learning methods have been demonstrated to be efficient tools for predicting moving processes while considering multiple geographic information sources. By developing a convolutional neural network (CNN) method, this study aimed to predict the pathway of dust storms that occur in arid regions in central and southern Asia. A total of 54 dust-storm events were extracted from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) product to train the CNN model and evaluate the prediction results. In addition to dust-storm data (aerosol optical depth (AOD) data), geographic context information including relative humidity, surface air temperature, surface wind direction, surface skin temperature, and surface wind speed was considered. These features were chosen using the random forest feature importance method and had feature importance values of 0.2, 0.1, 0.06, 0.03, and 0.02, respectively. The results show that the CNN model can promisingly predict the dust-transport pathway, such that for the 6, 12, 18, and 24-h time steps, the overall accuracy values were 0.9746, 0.975, 0.9751, and 0.9699, respectively; the F1 score values were 0.7497, 0.7525, 0.7476, and 0.6769, respectively; and the values of the kappa coefficient were 0.7369, 0.74, 0.7351, and 0.6625, respectively.

show abstract

Detection of the most frequent sources of dust storms in Iraq during 2020–2023 using space tools

Ismail,

Halos,

Al-Abudi

2025

Kuwait Journal of Science

View full text Add to dashboard Cite

Dust storm hazards

Cited by 3 publications

References 33 publications

Investigating the Role of the Low-Level Jet in Two Winters Severe Dust Rising in Southwest Iran

Investigating the Role of the Low-Level Jet in Two Winters Severe Dust Rising in Southwest Iran

Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas

Detection of the most frequent sources of dust storms in Iraq during 2020–2023 using space tools

Contact Info

Product

Resources

About