Articles | Volume 18, issue 8
https://doi.org/10.5194/amt-18-1981-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1981-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Apr 2025
Research article |
| 30 Apr 2025
| 30 Apr 2025
An analysis of cloud microphysical features over United Arab Emirates using multiple data sources
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Vesta Afzali Gorooh
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Duncan Axisa
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Chandrasekar Radhakrishnan
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Eun Yeol Kim
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Venkatachalam Chandrasekar
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Luca Delle Monache
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Related authors
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Brian C. Filipiak, David B. Wolff, Aaron Spaulding, Ali Tokay, Charles N. Helms, Adrian M. Loftus, Alexey V. Chibisov, Carl Schirtzinger, Mick J. Boulanger, Charanjit S. Pabla, Larry Bliven, Eun Yeol Kim, Francesc Junyent, V. Chandrasekar, Hein Thant, Branislav M. Notaros, Gustavo Britto Hupsel de Azevedo, and Diego Cerrai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-162, https://doi.org/10.5194/essd-2025-162, 2025
Preprint under review for ESSD
Short summary
Short summary
A GPM Ground Validation field campaign in Connecticut collected high-resolution microphysical and radar observations of winter precipitation. This field campaign was unique because there was a wide-ranging suite of instruments capable of observing all phases of precipitation co-located with comparable measurements. The observations provide an opportunity to verify and understand complex winter precipitation events through satellite data, microphysical processes, and numerical model simulations.
Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2025-1708, https://doi.org/10.5194/egusphere-2025-1708, 2025
Short summary
Short summary
We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
Marc Schneebeli, Andreas Leuenberger, Philipp J. Schmid, Jacopo Grazioli, Heather Corden, Alexis Berne, Patrick Kennedy, Jim George, Francesc Junyent, and V. Chandrasekar
EGUsphere, https://doi.org/10.5194/egusphere-2025-1702, https://doi.org/10.5194/egusphere-2025-1702, 2025
Short summary
Short summary
A new technique for the end-to-end calibration of weather radars is introduced. Highly precise artificial radar targets are generated with a radar target simulator and serve as a calibration reference for weather radar observables like reflectivity and Doppler velocity. The system allows to investigate and correct any biases associated with weather radar observations.
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
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
Short summary
Short summary
In a warm cloud seeding experiment hygroscopic particles are released to alter cloud processes to induce early raindrops. During the Cloud–Aerosol Interaction and Precipitation Enhancement Experiment, airborne mini aerosol mass spectrometers analyse the particles on which clouds form. The seeded clouds showed higher concentrations of chlorine and potassium, the oxidizing agents of flares. Small cloud droplet concentrations increased, and seeding particles were detected in deep cloud depths.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
Short summary
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.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
Short summary
Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Cited articles
Al Hosari, T., Al Mandous, A., Wehbe, Y., Shalaby, A., Al Shamsi, N., Al Naqbi, H., Al Yazeedi, O., Al Mazroui, A., and Farrah, S.: The UAE cloud seeding program: A statistical and physical evaluation, Atmosphere-Basel, 12, 1013, https://doi.org/10.3390/atmos12081013, 2021.
ATBD for Optimal Cloud Analysis Product: https://user.eumetsat.int/s3/eup-strapi-media/pdf_mtg_atbd_oca_b1ba8fc34c.pdf (last access: 18 November 2024), 2016.
Axisa, D. and DeFelice, T. P.: Modern and prospective technologies for weather modification activities: A look at integrating unmanned aircraft systems, Atmos. Res., 178, 114–124, https://doi.org/10.1016/j.atmosres.2016.03.005, 2016.
Baker, B., Mo, Q., Lawson, R. P., O'Connor, D., and Korolev, A.: The effects of precipitation on cloud droplet measurement devices, J. Atmos. Ocean. Tech., 26, 1404–1409, https://doi.org/10.1175/2009JTECHA1191.1, 2009.
Bartlett, J. T.: The growth of cloud droplets by coalescence, Q. J. Roy. Meteor. Soc., 92, 93–104, https://doi.org/10.1002/qj.49709239108, 1966.
Beall, C. M., Hill, T. C. J., DeMott, P. J., Köneman, T., Pikridas, M., Drewnick, F., Harder, H., Pöhlker, C., Lelieveld, J., Weber, B., Iakovides, M., Prokeš, R., Sciare, J., Andreae, M. O., Stokes, M. D., and Prather, K. A.: Ice-nucleating particles near two major dust source regions, Atmos. Chem. Phys., 22, 12607–12627, https://doi.org/10.5194/acp-22-12607-2022, 2022.
Brenguier, J. L., Bourrianne, T., Coelho, A. A., Isbert, J., Peytavi, R., Trevarin, D., and Weschler, P.: Improvements of droplet size distribution measurements with the Fast-FSSP (Forward Scattering Spectrometer Probe), J. Atmos. Ocean. Tech., 15, 1077–1090, https://doi.org/10.1175/1520-0426(1998)015<1077:IODSDM>2.0.CO;2, 1998.
Bruintjes, R. T.: A review of cloud seeding experiments to enhance precipitation and some new prospects, B. Am. Meteorol. Soc., 80, 805–820, https://doi.org/10.1175/1520-0477(1999)080<0805:AROCSE>2.0.CO;2, 1999.
Cooper, W. A., Bruintjes, R. T., and Mather, G. K.: Calculations pertaining to hygroscopic seeding with flares, J. Appl. Meteorol., 36, 1449–1469, https://doi.org/10.1175/1520-0450(1997)036<1449:CPTHSW>2.0.CO;2, 1997.
Defelice, T. P. and Axisa, D.: Developing the framework for integrating autonomous unmanned aircraft systems into cloud seeding activities, J. Aeronaut. Aerospace Eng., 5, 1–6, https://doi.org/10.4172/2168-9792.1000172, 2016.
DeFelice, T. P., Axisa, D., Bird, J. J., Hirst, C. A., Frew, E. W., Burger, R. P., Baumgardner, D., Botha, G., Havenga, H., Breed, D., Bornstein, S., Choate, C., Gomez-Faulk, C., and Rhodes, M.: Modern and prospective technologies for weather modification activities: A first demonstration of integrating autonomous uncrewed aircraft systems, Atmos. Res., 290, 106788, https://doi.org/10.1016/j.atmosres.2023.106788, 2023.
EUMETSAT: High-Rate SEVIRI Level 1.5 Image Data, EUMETSAT Data Centre [data set], https://data.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI-IODC, last access: 23 April 2025a.
EUMETSAT: Optimal Cloud Analysis – MSG, EUMETSAT Data Centre [data set], https://user.eumetsat.int/catalogue/EO:EUM:DAT:MSG:OCA-IODC, last access: 23 April 2025b.
Feng, Z., Leung, L. R., Houze Jr, R. A., Hagos, S., Hardin, J., Yang, Q., Han, B., and Fan, J.: Structure and evolution of mesoscale convective systems: Sensitivity to cloud microphysics in convection-permitting simulations over the United States, J. Adv. Model. Earth Sy., 10, 1470–1494, https://doi.org/10.1029/2018MS001305, 2018.
Flossmann, A. I., Manton, M., Abshaev, A., Bruintjes, R., Murakami, M., Prabhakaran, T., and Yao, Z.: Review of advances in precipitation enhancement research, B. Am. Meteorol. Soc., 100, 1465–1480, https://doi.org/10.1175/BAMS-D-18-0160.1, 2019.
Freud, E. and Rosenfeld, D.: Linear relation between convective cloud drop number concentration and depth for rain initiation, J. Geophys. Res.-Atmos., 117, D02207, https://doi.org/10.1029/2011JD016457, 2012.
Fu, D., Di Girolamo, L., Rauber, R. M., McFarquhar, G. M., Nesbitt, S. W., Loveridge, J., Hong, Y., van Diedenhoven, B., Cairns, B., Alexandrov, M. D., Lawson, P., Woods, S., Tanelli, S., Schmidt, S., Hostetler, C., and Scarino, A. J.: An evaluation of the liquid cloud droplet effective radius derived from MODIS, airborne remote sensing, and in situ measurements from CAMP2Ex, Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, 2022.
Geresdi, I., Xue, L., Chen, S., Wehbe, Y., Bruintjes, R., Lee, J. A., Rasmussen, R. M., Grabowski, W. W., Sarkadi, N., and Tessendorf, S. A.: Impact of hygroscopic seeding on the initiation of precipitation formation: results of a hybrid bin microphysics parcel model, Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, 2021.
Hadizadeh, M., Rahnama, M., Kamali, M., Kazemi, M., and Mohammadi, A.: A new method to estimate cloud effective radius using Meteosat Second Generation SEVIRI over Middle East, Adv. Space Res., 64, 933–943, https://doi.org/10.1016/j.asr.2019.05.035, 2019.
Hallett, J. and Mossop, S.: Production of secondary ice particles during the riming process, Nature, 249, 26–28, https://doi.org/10.1038/249026a0, 1974.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hirst, C. A., Bird, J. J., Burger, R., Havenga, H., Botha, G., Baumgardner, D., DeFelice, T., Axisa, D., and Frew, E. W.: An autonomous uncrewed aircraft system performing targeted atmospheric observation for cloud seeding operations, Field Robotics, 3, 687–724, https://doi.org/10.55417/fr.2023022, 2023.
Hussein, K. A., Alsumaiti, T. S., Ghebreyesus, D. T., Sharif, H. O., and Abdalati, W.: High-resolution spatiotemporal trend analysis of precipitation using satellite-based products over the United Arab Emirates, Water-Sui, 13, 2376, https://doi.org/10.3390/w13172376, 2021.
King, M. D., Tsay, S.-C., Platnick, S. E., Wang, M., and Liou, K.-N.: Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase, MODIS Algorithm Theoretical Basis Document No. ATBD-MOD-05 MOD06 – Cloud product, https://cimss.ssec.wisc.edu/dbs/China2011/Day2/Lectures/MOD06OD_Algorithm_Theoretical_Basis_Document.pdf (last access: 23 April 2025), 1997.
Korolev, A. V., Strapp, J. W., Isaac, G. A., and Nevzorov, A. N.: The Nevzorov airborne hot-wire LWC–TWC probe: Principle of operation and performance characteristics, J. Atmos. Ocean. Tech., 15, 1495–1510, https://doi.org/10.1175/1520-0426(1998)015<1495:TNAHWL>2.0.CO;2, 1998.
Korolev, A. V., Emery, E. F., Strapp, J. W., Cober, S. G., Isaac, G. A., Wasey, M., and Marcotte, D.: Small ice particles in tropospheric clouds: Fact or artifact? Airborne Icing Instrumentation Evaluation Experiment, B. Am. Meteorol. Soc., 92, 967–973, https://doi.org/10.1175/2010BAMS3141.1, 2011.
Kumar, K. N. and Suzuki, K.: Assessment of seasonal cloud properties in the United Arab Emirates and adjoining regions from geostationary satellite data, Remote Sens. Environ., 228, 90–104, https://doi.org/10.1016/j.rse.2019.04.024, 2019.
Lawson, R. P.: Effects of ice particles shattering on the 2D-S probe, Atmos. Meas. Tech., 4, 1361–1381, https://doi.org/10.5194/amt-4-1361-2011, 2011.
Lawson, R. P., Stewart, R. E., and Angus, L. J.: Observations and numerical simulations of the origin and development of very large snow-flakes, J. Atmos. Sci., 55, 32092–3229, https://doi.org/10.1175/1520-0469(1998)055<3209:oansot>2.0.co;2, 1998.
Lawson, R. P., Baker, B. A., Schmitt, C. G., and Jensen, T. L.: An overview of microphysical properties of Arctic clouds observed in May and July during FIRE, J. Geophys. Res.-Atmos., 106, 14989–15014, https://doi.org/10.1029/2000JD900789, 2001.
Lawson, R. P., O'Connor, D., Zmarzly, P., Weaver, K., Baker, B., Mo, Q., and Jonsson, H.: The 2D-S (stereo) probe: Design and preliminary tests of a new airborne, high speed, high-resolution particle imaging probe, J. Atmos. Ocean. Tech., 23, 1462–1477, https://doi.org/10.1175/JTECH1927.1, 2006.
Lawson, R. P., Gurganus, C., Woods, S., and Bruintjes, R.: Aircraft observations of cumulus microphysics ranging from the tropics to midlatitudes: Implications for a “new” secondary ice process, J. Atmos. Sci., 74, 2899–2920, https://doi.org/10.1175/JAS-D-17-0033.1, 2017.
Lazri, M., Ameur, S., Brucker, J. M., and Ouallouche, F.: Convective rainfall estimation from MSG/SEVIRI data based on different development phase duration of convective systems (growth phase and decay phase), Atmos. Res., 147, 38–50, https://doi.org/10.1016/j.atmosres.2014.04.019, 2014.
Lensky, I. M. and Drori, R.: A satellite-based parameter to monitor the aerosol impact on convective clouds, J. Appl. Meteorol. Clim., 46, 660–666, https://doi.org/10.1175/JAM2487.1, 2007.
Lensky, I. M. and Shiff, S.: Using MSG to monitor the evolution of severe convective storms over East Mediterranean Sea and Israel, and its response to aerosol loading, Adv. Geosci., 12, 95–100, https://doi.org/10.5194/adgeo-12-95-2007, 2007.
Liu, H. and Chandrasekar, V.: Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification, J. Atmos. Ocean. Tech., 17, 140–164, https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2, 2000.
Mather, G. K., Terblanche, D. E., Steffens, F. E., and Fletcher, L.: Results of the South African cloud-seeding experiments using hygroscopic flares, J. Appl. Meteorol., 36, 1433–1447, https://doi.org/10.1175/1520-0450(1997)036<1433:ROTSAC>2.0.CO;2, 1997.
Mazroui, A. A. and Farrah, S.: The UAE Seeks Leading Position in Global Rain Enhancement Research, J. Wea. Mod., 49, 54–55, https://doi.org/10.54782/jwm.v49i1.562, 2017.
Mecikalski, J. R., Watts, P. D., and Koenig, M.: Use of Meteosat Second Generation optimal cloud analysis fields for understanding physical attributes of growing cumulus clouds, Atmos. Res., 102, 175–190, https://doi.org/10.1016/j.atmosres.2011.06.023, 2011.
Morrison, H., Lawson, P., and Chandrakar, K. K.: Observed and Bin Model Simulated Evolution of Drop Size Distributions in High-Based Cumulus Congestus Over the United Arab Emirates, J. Geophys. Res.-Atmos., 127, e2021JD035711, https://doi.org/10.1029/2021JD035711, 2022.
Mossop, S. C.: The influence of drop size distribution on the production of secondary ice particles during graupel growth, Q. J. Roy. Meteor. Soc., 104, 323–330, https://doi.org/10.1002/qj.49710444007, 1978.
Murad, A. A., Al Nuaimi, H., and Al Hammadi, M.: Comprehensive assessment of water resources in the United Arab Emirates (UAE), Water Resour. Manag., 21, 1449–1463, https://doi.org/10.1007/s11269-006-9093-4, 2007.
Niranjan Kumar, K. and Ouarda, T. B. M. J.: Precipitation variability over UAE and global SST teleconnections, J. Geophys. Res.-Atmos., 119, 10–313, https://doi.org/10.1002/2014JD021724, 2014.
Ouarda, T. B., Charron, C., Kumar, K. N., Marpu, P. R., Ghedira, H., Molini, A., and Khayal, I.: Evolution of the rainfall regime in the United Arab Emirates, J. Hydrol., 514, 258–270, https://doi.org/10.1016/j.jhydrol.2014.04.032, 2014.
Pimentel, D., Berger, B., Filiberto, D., Newton, M., Wolfe, B., Karabinakis, E., Clark, S., Poon, E., Abbett, E., and Nandagopal, S.: Water resources: agricultural and environmental issues, BioScience, 54, 909–918, https://doi.org/10.1641/0006-3568(2004)054[0909:WRAAEI]2.0.CO;2, 2004.
Poulsen, C. A., Siddans, R., Thomas, G. E., Sayer, A. M., Grainger, R. G., Campmany, E., Dean, S. M., Arnold, C., and Watts, P. D.: Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR, Atmos. Meas. Tech., 5, 1889–1910, https://doi.org/10.5194/amt-5-1889-2012, 2012.
Pósfai, M., Axisa, D., Tompa, É., Freney, E., Bruintjes, R., and Buseck, P. R.: Interactions of mineral dust with pollution and clouds: An individual-particle TEM study of atmospheric aerosol from Saudi Arabia, Atmos. Res., 122, 347–361, https://doi.org/10.1016/j.atmosres.2012.12.001, 2013.
Pruppacher, H. R., Klett, J. D., and Wang, P. K.: Microphysics of clouds and precipitation, Aerosol Sci. Tech., 28, 381–382, https://doi.org/10.1080/02786829808965531, 1998.
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, World Scientific Publishing, Singapore-New Jersey-London-Hong Kong, ISBN 13 9789810227401, https://api.semanticscholar.org/CorpusID:60696486 (last access: 23 April 2025), 2000.
Rosenfeld, D.: TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall, Geophys. Res. Lett., 26, 3105–3108, https://doi.org/10.1029/1999GL006066, 1999.
Rosenfeld, D.: Suppression of rain and snow by urban and industrial air pollution, Science, 287, 1793–1796, https://doi.org/10.1126/science.287.5459.1793, 2000.
Rosenfeld, D. and Lensky, I. M.: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds, B. Am. Meteorol. Soc., 79, 2457–2476, https://doi.org/10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2, 1998.
Rosenfeld, D., Rudich, Y., and Lahav, R.: Desert dust suppressing precipitation: A possible desertification feedback loop, P. Natl. Acad. Sci. USA, 98, 5975–5980, https://doi.org/10.1073/pnas.101122798, 2001.
Rosenfeld, D., Axisa, D., Woodley, W. L., and Lahav, R.: A quest for effective hygroscopic cloud seeding, J. Appl. Meteorol. Clim., 49, 1548–1562, https://doi.org/10.1175/2010JAMC2307.1, 2010.
Sherif, M., Almulla, M., Shetty, A., and Chowdhury, R. K.: Analysis of rainfall, PMP and drought in the United Arab Emirates, Int. J. Climatol., 34, 1318–1328, https://doi.org/10.1002/joc.3768, 2014.
Silverman, B. A.: An independent statistical reevaluation of the South African hygroscopic flare seeding experiment, J. Appl. Meteorol., 39, 1373–1378, https://doi.org/10.1175/1520-0450(2000)039<1373:AISROT>2.0.CO;2, 2000.
Silverman, B. A.: A critical assessment of glaciogenic seeding of convective clouds for rainfall enhancement, B. Am. Meteorol. Soc., 82, 903–924, https://doi.org/10.1175/1520-0477(2001)082<0903:ACAOGS>2.3.CO;2, 2001.
Silverman, B. A.: A critical assessment of hygroscopic seeding of convective clouds for rainfall enhancement, B. Am. Meteorol. Soc., 84, 1219–1230, https://doi.org/10.1175/BAMS-84-9-1219, 2003.
Terblanche, D. E., Steffens, F. E., Fletcher, L., Mittermaier, M. P., and Parsons, R. C.: Toward the operational application of hygroscopic flares for rainfall enhancement in South Africa, J. Appl. Meteorol., 39, 1811–1821, https://doi.org/10.1175/1520-0450(2001)039<1811:TTOAOH>2.0.CO;2, 2000.
Vujović, D. and Protić, M.: The behavior of the radar parameters of cumulonimbus clouds during cloud seeding with AgI, Atmos. Res., 189, 33–46, https://doi.org/10.1016/j.atmosres.2017.01.014, 2017.
Wang, J., Yue, Z., Rosenfeld, D., Zhang, L., Zhu, Y., Dai, J., Yu, X., and Li, J.: The Evolution of an AgI Cloud-Seeding Track in Central China as Seen by a Combination of Radar, Satellite, and Disdrometer Observations, J. Geophys. Res.-Atmos., 126, e2020JD033914, https://doi.org/10.1029/2020JD033914, 2021.
Wang, Z., Letu, H., Shang, H., Zhao, C., Li, J., and Ma, R.: A supercooled water cloud detection algorithm using Himawari-8 satellite measurements, J. Geophys. Res.-Atmos., 124, 2724–2738, https://doi.org/10.1029/2018JD029784, 2019.
Watts, P., Mutlow, C., Baran, A., and Zavody, A.: Study on cloud properties derived from Meteosat Second Generation observations, EUMETSAT ITT no. 97/181, https://www-cdn.eumetsat.int/files/2020-04/pdf_sci_97181_msg-cloud-props.pdf (last access: 23 April 2025), 1998.
Watts, P. D., Bennartz, R., and Fell, F.: Retrieval of two-layer cloud properties from multispectral observations using optimal estimation, J. Geophys. Res.-Atmos., 116, D16203 https://doi.org/10.1029/2011JD015883, 2011.
Wehbe, Y. and Temimi, M.: A remote sensing-based assessment of water resources in the Arabian Peninsula, Remote Sens., 13, 247, https://doi.org/10.3390/rs13020247, 2021.
Wehbe, Y., Ghebreyesus, D., Temimi, M., Milewski, A., and Al Mandous, A.: Assessment of the consistency among global precipitation products over the United Arab Emirates, J. Hydrol. Reg. Stud., 12, 122–135, https://doi.org/10.1016/j.ejrh.2017.05.002, 2017.
Wehbe, Y., Temimi, M., Weston, M., Chaouch, N., Branch, O., Schwitalla, T., Wulfmeyer, V., Zhan, X., Liu, J., and Al Mandous, A.: Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data, Nat. Hazards Earth Syst. Sci., 19, 1129–1149, https://doi.org/10.5194/nhess-19-1129-2019, 2019.
Wehbe, Y., Temimi, M., and Adler, R. F.: Enhancing precipitation estimates through the fusion of weather radar, satellite retrievals, and surface parameters, Remote Sens., 12, 1342, https://doi.org/10.3390/rs12081342, 2020.
Wehbe, Y., Tessendorf, S. A., Weeks, C., Bruintjes, R., Xue, L., Rasmussen, R., Lawson, P., Woods, S., and Temimi, M.: Analysis of aerosol–cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates, Atmos. Chem. Phys., 21, 12543–12560, https://doi.org/10.5194/acp-21-12543-2021, 2021.
Wehbe, Y., Griffiths, S., Al Mazrouei, A., Al Yazeedi, O., and Al Mandous, A.: Rethinking water security in a warming climate: rainfall enhancement as an innovative augmentation technique, NPJ Clim. Atmos. Sci., 6, 171, https://doi.org/10.1038/s41612-023-00503-2, 2023.
Weston, M. J., Temimi, M., Nelli, N. R., Fonseca, R. M., Thota, M. S., and Valappil, V. K.: On the analysis of the low-level double temperature inversion over the United Arab Emirates: a case study during April 2019, IEEE Geosci. Remote S., 18, 346–350, https://doi.org/10.1109/LGRS.2020.2972597, 2020.
Woods, S., Lawson, R. P., Jensen, E., Bui, T. P., Thornberry, T., Rollins, A., Pfister, L., and Avery, M.: Microphysical properties of tropical tropopause layer cirrus, J. Geophys. Res.-Atmos., 123, 6053–6069, https://doi.org/10.1029/2017JD028068, 2018.
Woodley, W. L., Rosenfeld, D., and Silverman, B. A.: Results of on-top glaciogenic cloud seeding in Thailand. Part I: The demonstration experiment, J. Appl. Meteorol. Clim., 42, 920–938, https://doi.org/10.1175/1520-0450(2003)042<0920:ROOGCS>2.0.CO;2, 2003a.
Woodley, W. L., Rosenfeld, D., and Silverman, B. A.: Results of on-top glaciogenic cloud seeding in Thailand. Part II: Exploratory analyses, J. Appl. Meteorol., 42, 939–951, https://doi.org/10.1175/1520-0450(2003)042<0939:ROOGCS>2.0.CO;2, 2003b.
Zaremba, T. J., Rauber, R. M., Girolamo, L. D., Loveridge, J. R., and McFarquhar, G. M.: On the radar detection of cloud seeding effects in wintertime orographic cloud systems, J. Appl. Meteorol. Clim., 63, 27–45, https://doi.org/10.1175/JAMC-D-22-0154.1, 2024.
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where are the most intense thunderstorms on Earth?, B. Am. Meteorol. Soc., 87, 1057–1072, https://doi.org/10.1175/BAMS-87-8-1057, 2006.
Short summary
Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.
Water is a precious resource, and it is essential to monitor and predict the current and future...
