Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8659-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8659-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2022
Research article |
| 06 Jul 2022
| 06 Jul 2022
Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations
Sagar P. Parajuli
CORRESPONDING AUTHOR
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi
Arabia
Georgiy L. Stenchikov
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi
Arabia
Alexander Ukhov
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi
Arabia
Suleiman Mostamandi
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi
Arabia
Paul A. Kucera
National Center for Atmospheric Research, Boulder, CO 80305, USA
Duncan Axisa
Center for Western Weather and Water Extremes (CW3E), Scripps
Institution of Oceanography, University of California, San Diego, La Jolla,
California, USA
William I. Gustafson Jr.
Pacific Northwest National Laboratory (PNNL), Richland, WA 99354, USA
Yannian Zhu
School of Atmospheric Sciences, Nanjing University, 210023 Nanjing,
China
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Lindsay M. Sheridan, Raghavendra Krishnamurthy, Tien Manh Nguyen, Yi-Leng Chen, William I. Gustafson Jr., Ye Liu, Feng Hsiao, Rob K. Newsom, Preston Spicer, Evgueni Kassianov, Mikhail Pekour, Nicola Bodini, and Mark Severy
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Jiakun Liang, William I. Gustafson Jr., Dana R. Caulton, and Jennifer D. Small Griswold
EGUsphere, https://doi.org/10.5194/egusphere-2026-2786, https://doi.org/10.5194/egusphere-2026-2786, 2026
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Ilaria Quaglia, Daniele Visioni, Ewa M. Bednarz, Yunqian Zhu, Georgiy Stenchikov, Valentina Aquila, Cheng-Cheng Liu, Graham W. Mann, Yifeng Peng, Takashi Sekiya, Simone Tilmes, Xinyue Wang, Shingo Watanabe, Pengfei Yu, Jun Zhang, Wandi Yu, and Zhihong Zhuo
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On January 15, 2022, the Hunga volcano eruption released unprecedented amounts of water vapor into the atmosphere alongside a modest amount of SO2. In this work we analyse results from multiple Earth system models. The models agree that the eruption led to small negative radiative forcing from sulfate aerosols and that the contribution from water vapor was minimal. Therefore, the Hunga eruption cannot explain the exceptional surface warming observed in 2023.
Ewa M. Bednarz, Amy H. Butler, Xinyue Wang, Zhihong Zhuo, Wandi Yu, Georgiy Stenchikov, Matthew Toohey, and Yunqian Zhu
Atmos. Chem. Phys., 26, 197–215, https://doi.org/10.5194/acp-26-197-2026, https://doi.org/10.5194/acp-26-197-2026, 2026
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Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
Nat. Hazards Earth Syst. Sci., 26, 21–40, https://doi.org/10.5194/nhess-26-21-2026, https://doi.org/10.5194/nhess-26-21-2026, 2026
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Alexander Ukhov, Georgiy Stenchikov, Jordan Schnell, Ravan Ahmadov, Umberto Rizza, Georg Grell, and Ibrahim Hoteit
Geosci. Model Dev., 18, 9805–9825, https://doi.org/10.5194/gmd-18-9805-2025, https://doi.org/10.5194/gmd-18-9805-2025, 2025
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Zhihong Zhuo, Xinyue Wang, Yunqian Zhu, Wandi Yu, Ewa M. Bednarz, Eric Fleming, Peter R. Colarco, Shingo Watanabe, David Plummer, Georgiy Stenchikov, William Randel, Adam Bourassa, Valentina Aquila, Takashi Sekiya, Mark R. Schoeberl, Simone Tilmes, Jun Zhang, Paul J. Kushner, and Francesco S. R. Pausata
Atmos. Chem. Phys., 25, 13161–13176, https://doi.org/10.5194/acp-25-13161-2025, https://doi.org/10.5194/acp-25-13161-2025, 2025
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Yuanyuan Wu, Jihu Liu, Yannian Zhu, Yu Zhang, Yang Cao, Kang-En Huang, Boyang Zheng, Yichuan Wang, Yanyun Li, Quan Wang, Chen Zhou, Yuan Liang, Jianning Sun, Minghuai Wang, and Daniel Rosenfeld
Earth Syst. Sci. Data, 17, 3243–3258, https://doi.org/10.5194/essd-17-3243-2025, https://doi.org/10.5194/essd-17-3243-2025, 2025
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Based on a deep-learning method, we established a global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology. This aims to promote a comprehensive understanding of cloud dynamics and cloud–climate feedback. Closed mesoscale cellular convection (MCC) clouds occur more frequently at night, while suppressed cumulus clouds exhibit remarkable decreases. Solid stratus and MCC cloud types show clear seasonal variations.
Zhenhai Zhang, Vesta Afzali Gorooh, Duncan Axisa, Chandrasekar Radhakrishnan, Eun Yeol Kim, Venkatachalam Chandrasekar, and Luca Delle Monache
Atmos. Meas. Tech., 18, 1981–2003, https://doi.org/10.5194/amt-18-1981-2025, https://doi.org/10.5194/amt-18-1981-2025, 2025
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Chris J. Wright, Joel A. Thornton, Lyatt Jaeglé, Yang Cao, Yannian Zhu, Jihu Liu, Randall Jones II, Robert Holzworth, Daniel Rosenfeld, Robert Wood, Peter Blossey, and Daehyun Kim
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Aerosol particles influence clouds, which exert a large forcing on solar radiation and freshwater. To better understand the mechanisms by which aerosol influences thunderstorms, we look at the two busiest shipping lanes in the world, where recent regulations have reduced sulfur emissions by nearly an order of magnitude. We find that the reduction in emissions has been accompanied by a dramatic decrease in both lightning and the number of droplets in clouds over the shipping lanes.
Anton Lopatin, Oleg Dubovik, Georgiy Stenchikov, Ellsworth J. Welton, Illia Shevchenko, David Fuertes, Marcos Herreras-Giralda, Tatsiana Lapyonok, and Alexander Smirnov
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Mahen Konwar, Benjamin Werden, Edward C. Fortner, Sudarsan Bera, Mercy Varghese, Subharthi Chowdhuri, Kurt Hibert, Philip Croteau, John Jayne, Manjula Canagaratna, Neelam Malap, Sandeep Jayakumar, Shivsai A. Dixit, Palani Murugavel, Duncan Axisa, Darrel Baumgardner, Peter F. DeCarlo, Doug R. Worsnop, and Thara Prabhakaran
Atmos. Meas. Tech., 17, 2387–2400, https://doi.org/10.5194/amt-17-2387-2024, https://doi.org/10.5194/amt-17-2387-2024, 2024
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Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
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Mohamed Abdelkader, Georgiy Stenchikov, Andrea Pozzer, Holger Tost, and Jos Lelieveld
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We study the effect of injected volcanic ash, water vapor, and SO2 on the development of the volcanic cloud and the stratospheric aerosol optical depth (AOD). Both are sensitive to the initial injection height and to the aging of the volcanic ash shaped by heterogeneous chemistry coupled with the ozone cycle. The paper explains the large differences in AOD for different injection scenarios, which could improve the estimate of the radiative forcing of volcanic eruptions.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Feiyue Mao, Ruixing Shi, Daniel Rosenfeld, Zengxin Pan, Lin Zang, Yannian Zhu, and Xin Lu
Atmos. Chem. Phys., 22, 10589–10602, https://doi.org/10.5194/acp-22-10589-2022, https://doi.org/10.5194/acp-22-10589-2022, 2022
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Previous studies generally ignored the faint aerosols undetected by the CALIPSO layer detection algorithm because they are too optically thin. Here, we retrieved the faint aerosol extinction based on instantaneous CALIPSO observations with the constraint of SAGE data. The correlation and normalized root-mean-square error of the retrievals with independent SAGE data are 0.66 and 100.6 %, respectively. The minimum retrieved extinction at night can be extended to 10-4 km-1 with 125 % uncertainty.
Cited articles
Abdelkader, M., Metzger, S., Steil, B., Klingmüller, K., Tost, H., Pozzer, A., Stenchikov, G., Barrie, L., and Lelieveld, J.: Sensitivity of transatlantic dust transport to chemical aging and related atmospheric processes, Atmos. Chem. Phys., 17, 3799–3821, https://doi.org/10.5194/acp-17-3799-2017, 2017.
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation,
3, Sectional representation, J. Geophys. Res., 107, AAC 1-1–AAC 1-6,
https://doi.org/10.1029/2001JD000483, 2002.
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo,
K. M., and Silva-Dias, M. D.: Smoking rain clouds over the Amazon, Science,
303, 1337–1342, https://doi.org/10.1126/science.1092779, 2004.
Abbott, T. H. and Cronin, T. W.: Aerosol invigoration of atmospheric
convection through increases in humidity, Science, 371, 83–85,
https://doi.org/10.1126/science.abc5181, 2021.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, doi10.1126/science.245.4923.1227, 1989.
Ansmann, A., Mattis, I., Müller, D., Wandinger, U., Radlach, M.,
Althausen, D., and Damoah R.: Ice formation in Saharan dust over central
Europe observed with temperature/humidity/aerosol Raman lidar, J. Geophys.
Res., 110, D18S12, https://doi.org/10.1029/2004jd005000, 2005.
Anisimov, A., Tao, W., Stenchikov, G., Kalenderski, S., Prakash, P. J., Yang, Z.-L., and Shi, M.: Quantifying local-scale dust emission from the Arabian Red Sea coastal plain, Atmos. Chem. Phys., 17, 993–1015, https://doi.org/10.5194/acp-17-993-2017, 2017.
Bangalath, H. K. and Stenchikov, G.: Role of dust direct radiative effect
on the tropical rain belt over Middle East and North Africa: A
high-resolution AGCM study, J. Geophys. Res.-Atmos., 120, 4564–4584,
https://doi.org/10.1002/2015JD023122, 2015.
Chakraborty, S., Fu, R., Rosenfeld, D., and Massie, S. T.: The influence of
aerosols and meteorological conditions on the total rain volume of the
mesoscale convective systems over tropical continents, Geophys. Res. Lett.,
45, 13099–13106, https://doi.org/10.1029/2018GL080371,
2018.
Chapman, E. G., Gustafson Jr., W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., and Fast, J. D.: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources, Atmos. Chem. Phys., 9, 945–964, https://doi.org/10.5194/acp-9-945-2009, 2009.
Choobari, O. A.: Impact of aerosol number concentration on precipitation
under different precipitation rates, Meteorol. Appl., 25,
596–605, https://doi.org/10.1002/met.1724, 2018.
Choobari, O. A., Zawar-Reza, P., and Sturman, A.: The global distribution of
mineral dust and its impacts on the climate system: A review, Atmos. Res.,
138, 152–165, https://doi.org/10.1016/j.atmosres.2013.11.007,
2014.
Creamean, J. M., Suski, K. J., Rosenfeld, D., Cazorla, A., DeMott, P. J.,
Sullivan, R. C., White, A. B., Ralph, F. M., Minnis, P., Comstock, J. M. and
Tomlinson, J. M., and Prather, K. A.: Dust and biological aerosols from the
Sahara and Asia influence precipitation in the Western U.S, Science,
339, 1572–1578, https://doi.org/10.1126/science.1227279, 2013.
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013, https://doi.org/10.5194/essd-10-1987-2018, 2018.
Dennis, A. S.: Weather modification by cloud seeding, International Geophysics Series, 24, 670, https://digitalcommons.usu.edu/water_rep/670 (last access: 3 May 2020), 1980.
de Vries, A. J., Tyrlis, E., Edry, D., Krichak, S. O., Steil, B., and
Lelieveld, J.: Extreme precipitation events in the Middle East: Dynamics of
the Active Red Sea Trough, J. Geophys. Res., 118, 7087–7108, https://doi.org/10.1002/jgrd.50569, 2013.
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res., 105, 20673–20696, https://doi.org/10.1029/2000JD900282, 2000.
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011.
Dye, J. E. and Baumgardner, D.: Evaluation of the Forward
Scattering Spectrometer Probe. Part I: Electronic and Optical Studies, J.
Atmos. Ocean. Tech., 1, 329–344, 1984.
ECMWF: Archive Catalogue, ECMWF [data set], http://apps.ecmwf.int/archive-catalogue/?type=4v&class=od&stream=oper&expver=1, last access: 3 January 2021.
European Commission: EDGAR – Emissions Database for Global Atmospheric Research, European Commission [data set], http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: 3 August 2020.
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A.,
Martin, S. T., Yang, Y., Wang, J., Artaxo, P., and Barbosa, H. M.: Substantial
convection and precipitation enhancements by ultrafine aerosol particles,
Science, 359, 411–418, https://doi.org/10.1126/science.aan8461, 2018.
Farrar, J. T., Lentz, S. J., Churchill, J. H., Bouchard, P. R., Smith, J. C., Kemp, J. N., Lord, J., Allsup, G. P., and Hosom, D. S.: King Abdullah University of Science and Technology (KAUST) mooring deployment cruise and fieldwork report, fall 2008 R/V Oceanus voyage 449-5, October 9, 2008–October 14, 2008, WHOAS, https://doi.org/10.1575/1912/3012, 2009.
Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J.
C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone,
particulates, and aerosol direct radiative forcing in the vicinity of
Houston using a fully coupled meteorology-chemistry-aerosol model, J.
Geophys. Res., 111, D21305, https://doi.org/10.1029/2005JD006721, 2006.
Forkel, R., Werhahn, J., Hansen, A. B., McKeen, S., Peckham, S., Grell, G.,
and Suppan, P.: Effect of aerosol-radiation feedback on regional air quality
– A case study with WRF/Chem, Atmos. Environ., 53, 202–211, https://doi.org/10.1016/j.atmosenv.2011.10.009, 2012.
Freud, E., Rosenfeld, D., and Kulkarni, J. R.: Resolving both entrainment-mixing and number of activated CCN in deep convective clouds, Atmos. Chem. Phys., 11, 12887–12900, https://doi.org/10.5194/acp-11-12887-2011, 2011.
Gao, W., Fan, J., Easter, R. C., Yang, Q., Zhao, C., and Ghan, S. J.:
Coupling spectral-bin cloud microphysics with the MOSAIC aerosol model in
WRF-Chem: Methodology and results for marine stratocumulus clouds, J. Adv.
Model. Earth Sy., 8, 1289–1309, https://doi.org/10.1002/2016MS000676,
2016.
Gibbons, J. D. and Chakraborti, S.: Nonparametric Statistical Inference, 5th
Edn., Boca Raton, FLn Chapman & Hall/CRC Press, Taylor & Francis Group, http://www.ru.ac.bd/stat/wp-content/uploads/sites/25/2019/03/501_13_Gibbons_Nonparametric_statistical_inference.pdf (last access: 4 July 2022),
2011.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Gong, S. L.: A parameterization of sea-salt aerosol source function for sub- and super-micron particles, Global Biogeochem. Cy., 17, 1097, https://doi.org/10.1029/2003GB002079, 2003.
Grabowski, W. W. and Morrison, H.: Do Ultrafine Cloud Condensation Nuclei
Invigorate Deep Convection?, J. Atmos. Sci., 77, 2567–2583,
https://doi.org/10.1175/JAS-D-20-0012.1, 2020.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model,
Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Gustafson, W. I., Chapman, E. G., Ghan, S. J., Easter, R. C., and Fast, J.
D.: Impact on modeled cloud characteristics due to simplified
treatment of uniform cloud condensation nuclei during NEAQS 2004, Geophys.
Res. Lett., 34, L19809, https://doi.org/10.1029/2007GL030021,
2007.
Han, Y., Fang, X., Zhao, T., Bai, H., Kang, S., and Song, L.: Suppression of
precipitation by dust particles originated in the Tibetan Plateau, Atmos.
Environ., 43, 568–574, https://doi.org/10.1016/j.atmosenv.2008.10.018, 2009.
Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate
response, J. Geophys. Res.-Atmos., 102, 6831–6864, https://doi.org/10.1029/96JD03436, 1997.
Holben, B. N., Eck, T. F., Slutsker, I. A., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Hollander, M. and Wolfe, D. A.: Nonparametric Statistical Methods, Hoboken,
NJ, John Wiley & Sons, Inc., 1999.
Hong, S.-Y., Yign, N., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Hsu, N. C., Tsay, S.-C., King, M. D., and Herman, J. R.: Aerosol properties over bright-reflecting source regions, IEEE T. Geosci. Remote Sens., 42, 557–569, https://doi.org/10.1109/TGRS.2004.824067, 2004.
Hsu, N. C., Jeong, M.-J., Bettenhausen, C., Sayer, A. M., Hansell, R., Seftor, C. S., Huang, J., and Tsay, S.-C.: Enhanced Deep Blue aerosol retrieval algorithm: The second generation, J. Geophys. Res.-Atmos., 118, 9296–9315, https://doi.org/10.1002/jgrd.50712, 2013.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.:
GPM IMERG Late Precipitation L3 1 day 0.1 degree × 0.1 degree V06, edited by:
Savtchenko, A., Greenbelt, MD, Goddard Earth Sciences Data and
Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERGDL/DAY/06, 2019.
Iacono, M. J., Mlawer, E. J., Clough, S. A., and Morcrette, J.-J.: Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3, J. Geophys. Res., 105, 14873–14890, https://doi.org/10.1029/2000JD900091, 2000.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008.
Jacobson, M. Z. and Kaufman, Y. J.: Wind reduction by aerosol particles,
Geophys. Res. Lett., 33, L24814, https://doi.org/10.1029/2006GL027838, 2006.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
Jha, V., Cotton, W., R., Carrió, G. G., and Walko, R.:
Sensitivity Studies on the Impact of Dust and Aerosol Pollution Acting as
Cloud Nucleating Aerosol on Orographic Precipitation in the Colorado River
Basin, Adv. Meteorol., 2018, 3041893, https://doi.org/10.1155/2018/3041893, 2018.
Jha, V., Cotton, W. R., Carrió, G. G., and Walko, R.: Seasonal
estimates of the impacts of aerosol and dust pollution on orographic
precipitation in the Colorado River Basin, Phys. Geogr., 42, 73–97,
https://doi.org/10.1080/02723646.2020.1792602, 2021.
Jimenez, P. A., Dudhia, J., Gonzalez–Rouco, J. F., Navarro, J., Montavez, J. P., and Garcia–Bustamante, E.: A revised scheme for the WRF surface layer formulation, Mon. Weather. Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Jin, Q., Wei, J., Yang, Z.-L., Pu, B., and Huang, J.: Consistent response of Indian summer monsoon to Middle East dust in observations and simulations, Atmos. Chem. Phys., 15, 9897–9915, https://doi.org/10.5194/acp-15-9897-2015, 2015.
Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., and Bangalath, H.: The impact of dust storms on the Arabian Peninsula and the Red Sea, Atmos. Chem. Phys., 15, 199–222, https://doi.org/10.5194/acp-15-199-2015, 2015.
Joodaki, G., Wahr, J., and Swenson, S.: Estimating the human contribution to
groundwater depletion in the Middle East, from GRACE data, land surface
models, and well observations, Water Resour. Res., 50, 2679–2692,
https://doi.org/10.1002/2013WR014633, 2014.
Jordan, A. K., Zaitchik, B. F., Gnanadesikan, A., Kim, D., and Badr, H. S.:
Strength of Linkages Between Dust and Circulation Over North Africa: results
from a coupled modeling system with active dust, J. Geophys. Res.-Atmos., 125, e2019JD030961, https://doi.org/10.1029/2019JD030961, 2020.
Kalenderski, S. and Stenchikov, G.: High-resolution regional
modeling of summertime transport and impact of African dust over the Red Sea
and Arabian Peninsula, J. Geophys. Res.-Atmos., 121, 6435–6458,
https://doi.org/10.1002/2015JD024480, 2016.
Kawecki, S. and Steiner, A. L.: The influence of aerosol
hygroscopicity on precipitation intensity during a mesoscale convective
event, J. Geophys. Res.-Atmos., 123, 424–442, https://doi.org/10.1002/2017JD026535, 2018.
Karydis, V. A., Kumar, P., Barahona, D., Sokolik, I. N., and
Nenes, A.: On the effect of dust particles on global cloud condensation
nuclei and cloud droplet number, J. Geophys. Res., 116, D23204,
https://doi.org/10.1029/2011JD016283, 2011.
Khan, B., Stenchikov, G., Weinzierl, B., Kalenderski, S., and Osipov, S.:
Dust plume formation in the free troposphere and aerosol size distribution
during the Saharan Mineral Dust Experiment in North Africa, Tellus B, 67, https://doi.org/10.3402/tellusb.v67.27170, 2015.
Konare, A., Zakey, A. S., Solmon, F., Giorgi, F., Rauscher, S., Ibrah, S.,
and Bi, X.: A regional climate modeling study of the effect of desert dust
on the West African monsoon, J. Geophys. Res., 113, D12206,
https://doi.org/10.1029/2007JD009322, 2008.
Klingmüller, K., Pozzer, A., Metzger, S., Stenchikov, G. L., and Lelieveld, J.: Aerosol optical depth trend over the Middle East, Atmos. Chem. Phys., 16, 5063–5073, https://doi.org/10.5194/acp-16-5063-2016, 2016.
Klingmüller, K., Lelieveld, J., Karydis, V. A., and Stenchikov, G. L.: Direct radiative effect of dust–pollution interactions, Atmos. Chem. Phys., 19, 7397–7408, https://doi.org/10.5194/acp-19-7397-2019, 2019.
Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y.:
Aerosol invigoration and restructuring of Atlantic convective
clouds, Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005GL023187, 2005.
Koren, I., Martins, J. V., Remer, L. A., and Afargan, H.: Smoke invigoration
versus inhibition of clouds over the Amazon, Science, 321, 946–949,
https://doi.org/10.1126/science.1159185, 2008.
Koren, I., Dagan, G., and Altaratz, O.: From aerosol-limited to invigoration
of warm convective clouds, Science, 344, 1143–1146, https://doi.org/10.1126/science.1252595, 2014.
Krauss, T. W., Sinkevich, A. A., and Ghulam, A. S.: Effects of feeder cloud
merging on storm development in Saudi Arabia, Journal of King Abdulaziz
University: Metrology, Environment and Arid Land Agricultural
Sciences, 142, 1–33, https://doi.org/10.4197/Met.22-2.2,
2011.
Kravitz, B., Wang, H., Rasch, P. J., Morrison, H., and Solomon, A. B.: Process-model simulations of cloud albedo enhancement by aerosols in the Arctic, Philos. T. R. Soc. A, 372, 20140052, https://doi.org/10.1098/rsta.2014.0052, 2014.
Kucera, P., Axisa, D., Burger, R. P., Collins, D. R., Li, R., Chapman, M.,
Posada, R., Krauss, T. W., and Ghulam, A. S.: Features of the Weather
Modification Assessment Project in Southwest Region of Saudi Arabia,
Journal Weather Modification, 42, 78–103, 2010.
Lau, W. K. M., Kim, M.-K., Kim, K.-M., and Lee, W.-S.: Enhanced surface
warming and accelerated snow melt in the Himalayas and Tibetan Plateau
induced by absorbing aerosols, Environ. Res. Lett., 5, 025204, https://doi.org/10.1088/1748-9326/5/2/025204, 2010.
Lee, S. S.: Effect of Aerosol on Circulations and Precipitation in Deep
Convective Clouds, J. Atmos.
Sci., 69, 1957–1974, https://doi.org/10.1175/JAS-D-11-0111.1, 2012.
Li, R., Min, Q., and Harrison, L. C.: A Case Study: The Indirect Aerosol
Effects of Mineral Dust on Warm Clouds, J. Atmos. Sci., 67,
805–816, https://doi.org/10.1175/2009JAS3235.1, 2010.
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.: Long-term
impacts of aerosols on the vertical development of clouds and precipitation,
Nat. Geosci., 4, 888–894, https://doi.org/10.1038/ngeo1313,
2011.
Lim, K. S. and Hong, S.: Development of an Effective Double-Moment Cloud
Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for
Weather and Climate Models, Mon. Weather Rev., 138, 1587–1612, https://doi.org/10.1175/2009MWR2968.1, 2010.
Liu, Z., Ostrenga, D., Teng, W., and Kempler, S.: Tropical Rainfall Measuring
Mission (IMERG) Precipitation Data and Services for Research and
Applications, B. Am. Meteorol. Soc., 93, 1317–1325, https://doi.org/10.1175/BAMS-D-11-00152.1, 2012.
Lohmann, U. and Feichter, J.: Can the direct and semi-direct aerosol effect
compete with the indirect effect on a global scale?, Geophys. Res. Lett.,
28, 159–161, https://doi.org/10.1029/2000GL012051, 2001.
Lopatin, A., Dubovik, O., Fuertes, D., Stenchikov, G., Lapyonok, T., Veselovskii, I., Wienhold, F. G., Shevchenko, I., Hu, Q., and Parajuli, S.: Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations, Atmos. Meas. Tech., 14, 2575–2614, https://doi.org/10.5194/amt-14-2575-2021, 2021.
Mahmoud, M. T., Al-Zahrani, M. A., and Sharif, H. O.: Assessment
of global precipitation measurement satellite products over Saudi Arabia, J.
Hydrol., 559, 1–12, https://doi.org/10.1016/j.jhydrol.2018.02.015, 2018.
Mazroui, A. A. and Farrah, S.: The UAE seeks leading position in global rain
enhancement research, Journal of Weather Modification, 49, 54–55, 2017.
Miller, S. T. K., Keim, B. D., Talbot, R. W., and Mao, H.: Sea breeze:
Structure, forecasting, and impacts, Rev. Geophys., 41, 1011,
https://doi.org/10.1029/2003RG000124, 2003.
Monahan, E. C., Spiel, D. E., and Davidson, K. L.: A Model of Marine Aerosol Generation Via Whitecaps and Wave Disruption, in: Oceanic Whitecaps, edited by: Monahan, E. C. and Niocaill, G. M., Oceanographic Sciences Library, Springer, Dordrecht, 2, 167–174, https://doi.org/10.1007/978-94-009-4668-2_16, 1986.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics
on the Development of Trailing Stratiform Precipitation in a Simulated
Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather
Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1,
2009.
Mostamandi, S., Predybaylo, E., Osipov, S., Zolina, O., Gulev, S., Parajuli, S., and Stenchikov, G.: Sea Breeze Geoengineering to Increase Rainfall over the Arabian Red Sea Coastal Plains, J. Hydrometeorol., 23, 3–24, https://doi.org/10.1175/JHM-D-20-0266.1, 2022.
Min, Q.-L., Li, R., Lin, B., Joseph, E., Wang, S., Hu, Y., Morris, V., and Chang, F.: Evidence of mineral dust altering cloud microphysics and precipitation, Atmos. Chem. Phys., 9, 3223–3231, https://doi.org/10.5194/acp-9-3223-2009, 2009.
NASA: MODIS AOD data, LAADS DAAC [data set], https://ladsweb.modaps.eosdis.nasa.gov/, last access: 4 July 2022a.
NASA: MERRA-2 and IMERG data, GES DISC [data set], https://disc.gsfc.nasa.gov/, last access: 4 July 2022b.
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., and Kim, H.: Dust emission
modeling using a new high-resolution dust source function in WRF-Chem with
implications for air quality, J. Geophys. Res.-Atmos., 124, 10109–10133,
https://doi.org/10.1029/2019JD030248, 2019.
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Shevchenko, I., Dubovik, O., and Lopatin, A.: Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations, Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, 2020.
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Mostamandi, S., Kucera, P., Axisa, D., Gustafson, W., and Zhu, Y.: Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations, KAUST Research Repository [data set], https://doi.org/10.25781/KAUST-ZZ3WX, 2022.
Rémy, S., Benedetti, A., Bozzo, A., Haiden, T., Jones, L., Razinger, M., Flemming, J., Engelen, R. J., Peuch, V. H., and Thepaut, J. N.: Feedbacks of dust and boundary layer meteorology during a dust storm in the eastern Mediterranean, Atmos. Chem. Phys., 15, 12909–12933, https://doi.org/10.5194/acp-15-12909-2015, 2015.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's modern-era retrospective
analysis for research and applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Roberts, G. C. and Nenes, A.: A Continuous-Flow Streamwise
Thermal-Gradient CCN Chamber for Atmospheric Measurements, Aerosol Sci.
Tech., 39, 206–221, https://doi.org/10.1080/027868290913988, 2005.
Rosenfeld, D., Rudich, Y., and Lahav, R.: Desert dust suppression
precipitation: A possible desertification feedback loop, P. Natl. Acad.
Sci. USA, 98, 5975–5980, https://doi.org/10.1073/pnas.101122798, 2001.
Rosenfeld, D., Liu, G., Yu, X., Zhu, Y., Dai, J., Xu, X., and Yue, Z.: High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager, Atmos. Chem. Phys., 14, 2479–2496, https://doi.org/10.5194/acp-14-2479-2014, 2014.
Rosenfeld, D., Zheng, Y., Hashimshoni, E., Pöhlker, M. L., Jefferson, A., Pöhlker, C., Yu, X., Zhu, Y., Liu, G., Yue, Z., Fischman, B., Li, Z., Giguzin, D., Goren, T., Artaxo, P., Barbosa, H. M. J., Pöschl, U., and Andreae, M. O.: Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers, P. Natl. Acad. Sci. USA, 113, 5828–5834, https://doi.org/10.1073/pnas.1514044113, 2016.
Sinkevich, A. A. and Krauss, T. W.: Cloud modification in Saudi Arabia:
Statistical estimation of the results, Russ. Meteorol. Hydrol.,
35, 378–385, https://doi.org/10.3103/S1068373910060038, 2010.
Simpson, J. E.: Sea breeze and local winds, Cambridge University Press,
1994.
Solomos, S., Kallos, G., Kushta, J., Astitha, M., Tremback, C., Nenes, A., and Levin, Z.: An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation, Atmos. Chem. Phys., 11, 873–892, https://doi.org/10.5194/acp-11-873-2011, 2011.
Spurny, K. R.: Atmospheric Condensation Nuclei P. J. Coulier 1875 and J.
Aitken 1880 (Historical Review), Aerosol Sci. Tech., 32, 243–248,
https://doi.org/10.1080/027868200303777, 2000.
Stull, R.: Meteorology for scientists and engineers, Brooks/Cole, https://www.eoas.ubc.ca/books/Practical_Meteorology/mse3.html (last access: 4 July 2022),
2000.
Tai, Y., Liang, H., Zaki, A., El Hadri, N., Abshaev, A. M.,
Huchunaev, B. M., Griffiths, S., Jouiad, M., and Zou, L.: Core/Shell Microstructure Induced Synergistic Effect for Efficient Water-Droplet Formation and Cloud-Seeding Application, ACS
Nano, 11, 12318–12325, https://doi.org/10.1021/acsnano.7b06114, 2017.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model in the WRF model, 20th conference on
weather analysis and forecasting/16th conference on numerical weather
prediction, 11–15, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm (last access: 4 July 2022), 2004.
Trinh, T.-A., Feeny, S., and Posso, A.: Rainfall shocks and child health: the
role of parental mental health, Clim. Dev., 13, 34–48,
https://doi.org/10.1080/17565529.2020.1716672, 2020.
Tuccella, P., Curci, G., Grell, G. A., Visconti, G., Crumeyrolle, S., Schwarzenboeck, A., and Mensah, A. A.: A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data, Geosci. Model Dev., 8, 2749–2776, https://doi.org/10.5194/gmd-8-2749-2015, 2015.
Twohy, C. H.: Measurements of Saharan Dust in Convective Clouds over the
Tropical Eastern Atlantic Ocean, J. Atmos. Sci., 72, 75–81, https://doi.org/10.1175/JAS-D-14-0133.1, 2015.
Twomey, S. A.: Aerosols, clouds and radiation, Atmos. Environ. A-Gen., 25,
2435–2442, https://doi.org/10.1016/0960-1686(91)90159-5, 1991.
Ukhov, A., Mostamandi, S., da Silva, A., Flemming, J., Alshehri, Y., Shevchenko, I., and Stenchikov, G.: Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations, Atmos. Chem. Phys., 20, 9281–9310, https://doi.org/10.5194/acp-20-9281-2020, 2020.
Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.
Yang, Q., Gustafson Jr., W. I., Fast, J. D., Wang, H., Easter, R. C., Morrison, H., Lee, Y.-N., Chapman, E. G., Spak, S. N., and Mena-Carrasco, M. A.: Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem, Atmos. Chem. Phys., 11, 11951–11975, https://doi.org/10.5194/acp-11-11951-2011, 2011.
Yang, Q., Gustafson Jr., W. I., Fast, J. D., Wang, H., Easter, R. C., Wang, M., Ghan, S. J., Berg, L. K., Leung, L. R., and Morrison, H.: Impact of natural and anthropogenic aerosols on stratocumulus and precipitation in the Southeast Pacific: a regional modelling study using WRF-Chem, Atmos. Chem. Phys., 12, 8777–8796, https://doi.org/10.5194/acp-12-8777-2012, 2012.
Yin, Y. and Chen, L.: The effects of heating by transported dust layers on cloud and precipitation: a numerical study, Atmos. Chem. Phys., 7, 3497–3505, https://doi.org/10.5194/acp-7-3497-2007, 2007.
Yin, Y., Wurzler, S., Levin, Z., and Reisin, T. G.: Interactions of mineral
dust particles and clouds: Effects on precipitation and cloud optical
properties, J. Geophys. Res., 107, 4724, https://doi.org/10.1029/2001JD001544, 2002.
Yue, Z., Rosenfeld, D., Liu, G., Dai, J., Yu, X., Zhu, Y., Hashimshoni, E., Xu, X., Hui, Y., and Lauer, O.: Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical, and CCN Properties from SNPP/VIIRS Satellite Data, J. Appl. Meteorol. Clim., 58, 887–902, https://doi.org/10.1175/JAMC-D-18-0144.1, 2019.
Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical
mechanism for large-scale applications, J. Geophys. Res., 104, 30387–30415, https://doi.org/10.1029/1999JD900876, 1999.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for
Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys.
Res., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008.
Zeinab S. Z., Steiner, A., Zakey, A. S., Shalaby, A., and Wahab, M. M. A.:
An exploration of the aerosol indirect effects in East Asia using a regional
climate model, Atmosfera, 33, 87–103, https://doi.org/10.20937/ATM.52604, 2020.
Zhang, Y., He, J., Zhu, S., and Gantt, B.: Sensitivity of simulated chemical
concentrations and aerosol-meteorology interactions to aerosol treatments
and biogenic organic emissions in WRF/Chem, J. Geophys. Res.-Atmos., 121, 6014–6048, https://doi.org/10.1002/2016JD024882,
2016.
Zhang, Y., Wang, K., and He, J.: Multi-year application of WRF-CAM5 over East
Asia – Part II: Interannual variability, trend analysis, and aerosol indirect
effects, Atmos. Environ., 165, 222–239, https://doi.org/10.1016/j.atmosenv.2017.06.029, 2017.
Zhao, B., Wang, Y., Gu, Y., Liou, K.-N., Jiang, J. H., Fan, J., Liu, X.,
Huang, L., and Yung, Y. L.: Ice nucleation by aerosols from anthropogenic
pollution, Nat. Geosci., 12, 602–607, https://doi.org/10.1038/s41561-019-0389-4, 2019.
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson Jr., W. I., Fast, J. D., and Easter, R.: The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments, Atmos. Chem. Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010.
Zhao, C., Liu, X., Ruby Leung, L., and Hagos, S.: Radiative impact of mineral dust on monsoon precipitation variability over West Africa, Atmos. Chem. Phys., 11, 1879–1893, https://doi.org/10.5194/acp-11-1879-2011, 2011.
Zheng, Y. and Rosenfeld, D.: Linear relation between convective cloud base height and updrafts and application to satellite retrievals, Geophys. Res. Lett., 42, 6485–6491, https://doi.org/10.1002/2015GL064809, 2015.
Short summary
Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Rainfall affects the distribution of surface- and groundwater resources, which are constantly...
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