Articles | Volume 20, issue 5
https://doi.org/10.5194/acp-20-2967-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-20-2967-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Convective distribution of dust over the Arabian Peninsula: the impact of model resolution
Jennie Bukowski
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
Susan C. van den Heever
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
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- Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area Q. Liao et al. 10.1016/j.scitotenv.2024.175233
- Monitoring and simulation of a 7-day dust episode and associated dust radiative forcing over the Middle East via synergy of satellite observations, reanalysis datasets and regional/numerical models K. Mohammadpour et al. 10.1016/j.atmosres.2025.107948
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15 citations as recorded by crossref.
- Simulation of convective turbulent dust emissions over Horqin Sandy Land, China G. Liang et al. 10.1016/j.aosl.2025.100711
- Experimental modeling of atmospheric discharge phenomena and charged dust particle interactions M. Abdelaal et al. 10.3389/fspas.2024.1347048
- A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling S. Albers et al. 10.5194/amt-13-3235-2020
- Minute-scale convective dust events are overlooked in urban air quality monitoring K. Ardon-Dryer 10.1038/s43247-025-02836-7
- Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region N. Hamzeh et al. 10.3390/atmos12010125
- A Comprehensive Review of Dust Storm Detection and Prediction Techniques: Leveraging Satellite Data, Ground Observations, and Machine Learning A. Mutawa et al. 10.1109/ACCESS.2025.3541075
- Seasonal simulations of summer aerosol optical depth over the Arabian Peninsula using WRF‐Chem: Validation, climatology, and variability R. Karumuri et al. 10.1002/joc.7396
- From cause to consequence: examining the historic April 2024 rainstorm in the United Arab Emirates through the lens of climate change D. Francis et al. 10.1038/s41612-025-01073-1
- Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module A. Ukhov et al. 10.5194/gmd-14-473-2021
- Rain, Wind, and Dust Connections in the Sahel G. Bergametti et al. 10.1029/2021JD035802
- A statistical approach for identification of dust-AOD hotspots climatology and clustering of dust regimes over Southwest Asia and the Arabian Sea K. Mohammadpour et al. 10.1016/j.apr.2022.101395
- Impact of dust radiation effect on simulations of temperature and wind – A case study in Taklimakan Desert H. Li & C. Wang 10.1016/j.atmosres.2022.106163
- Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area Q. Liao et al. 10.1016/j.scitotenv.2024.175233
- Monitoring and simulation of a 7-day dust episode and associated dust radiative forcing over the Middle East via synergy of satellite observations, reanalysis datasets and regional/numerical models K. Mohammadpour et al. 10.1016/j.atmosres.2025.107948
- Direct Radiative Effects in Haboobs J. Bukowski & S. van den Heever 10.1029/2021JD034814
2 citations as recorded by crossref.
Latest update: 29 Oct 2025
Short summary
This paper seeks to better our understanding of how dust storms are represented in a weather model. Depending on how well the model can represent the storm, it can change the dust forecast significantly. This is important for predictions of air quality and visibility; as dust can heat and cool the air in its environment, it is also crucial for calculating the Earth's energy budget. Here, we communicate the uncertainty in a dust model and the effect that it may have on dust forecasts.
This paper seeks to better our understanding of how dust storms are represented in a weather...
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