Two new multirotor uncrewed aerial vehicles (UAVs) for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project
Open accessPublished version of a preprint
Abstract
Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CLOUDLAB investigates ice microphysical processes in supercooled low-stratus clouds over the Swiss Plateau through controlled glaciogenic seeding using uncrewed aerial vehicles (UAVs) during three winter campaigns.
“It investigates ice microphysical processes in supercooled low-stratus clouds over the Swiss Plateau through controlled glaciogenic seeding using uncrewed aerial vehicles (UAVs) during three winter campaigns (Henneberger et al., 2023; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
The Introduction and Discussion and conclusions describe CLOUDLAB’s controlled UAV seeding of supercooled stratus clouds to investigate ice formation and growth. Section 2.5 places the field site on the Swiss Plateau, but says two winter campaigns had been conducted and a third was planned; it does not establish that the investigation had taken place during three campaigns. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe CLOUDLAB UAV delivery system was validated in clear-sky (or out-of-cloud) experiments that aimed to characterize the dispersion and concentration of seeding particles without interference from cloud microphysical processes, providing a baseline for comparison with in-cloud seeding missions.
“The UAV delivery system was validated in clear-sky (or out-of-cloud) experiments that aimed to characterize the dispersion and concentration of seeding particles without interference from cloud microphysical processes, providing a baseline for comparison with in-cloud seeding missions (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredCross-validation protocols compared POPS aerosol data with TBS measurement, ensuring consistency in plume characterization.
“Crossvalidation protocols compared POPS aerosol data with TBS measurement, ensuring consistency in plume characterization (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Section 3.1 and Appendix B compare aerosol measurements from POPS instruments mounted on the UAV and TBS, finding generally good agreement in laboratory tests. They do not establish that cross-validation ensured consistent plume characterization; Section 5.2 instead reports fluctuations in some in-cloud TBS measurements. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe second CLOUDLAB campaign (2022-2023) refined technical execution, optimizing UAV performance (e.g., icing resistance, flare ignition protocols) and validating aerosol measurements from the POPS to characterize seeding particle emissions.
“The second campaign (2022-2023) refined technical execution, optimizing UAV performance (e.g., icing resistance, flare ignition protocols) and validating aerosol measurements from the POPS to characterize seeding particle emissions (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRadar reflectivity and HOLIMO-derived growth timescales were aligned with UAV trajectory data to isolate seeding-induced microphysical responses.
“Radar reflectivity and HOLIMO-derived growth timescales were aligned with UAV trajectory data to isolate seeding-induced microphysical responses (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Section 5.2 links four peaks in POPS and HOLIMO measurements to the seeding UAV’s four flight legs and compares plume-arrival timing with a calculated advection time to distinguish the plume from natural cloud variation. The paper does not describe aligning radar reflectivity or HOLIMO-derived growth timescales with UAV trajectory data; the timescale reported here is for advection, not growth. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
By inducing controlled ice nucleation, the CLOUDLAB project investigated microphysical processes such as the WBF mechanism and assessed how environmental variables (e.g., temperature, wind speed) influence AgI's efficacy as INP.
“By inducing controlled ice nucleation, the project investigated microphysical processes such as the WBF mechanism and assessed how environmental variables (e.g., temperature, wind speed) influence AgI's efficacy as INP (Henneberger et al., 2023; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
The Introduction and Discussion and conclusions describe CLOUDLAB seeding as a way to initiate ice formation and study ice-crystal growth and aerosol–cloud microphysics. Section 2.4 says temperature and wind speed informed flight planning, including temperatures suitable for ice nucleation with silver iodide; it does not report an assessment of how those variables affect AgI's effectiveness as an INP. The passages also do not identify the WBF mechanism as a process investigated in this paper. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe other CLOUDLAB UAV performed measurements, with its POPS validated to minimize rotor-induced turbulence effects, ensuring reliable aerosol and cloud property data.
“The other UAV performed measurements, with its POPS validated to minimize rotor-induced turbulence effects, ensuring reliable aerosol and cloud property data (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Section 2.2 identifies the measurement UAV and its POPS, and Section 3.2 reports tests of rotor effects on aerosol measurements. Those tests found little effect on mean total particle concentration, but rotor-related differences in particle-size measurements required additional uncertainty allowances. The cited validation does not establish reliable cloud-property data. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe POPS sensors experienced coincidence errors when particle concentrations exceeded ~10^4 cm^-3, overlapping scattering signals led to undercounting or mis-sizing unless baseline scattering thresholds were applied to filter data.
“For instance, the POPS sensors experienced coincidence errors when particle concentrations exceeded ∼ 10 4 cm -3 , overlapping scattering signals led to undercounting or mis-sizing unless baseline scattering thresholds were applied to filter data (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Section 3.4 (page not provided) describes coincidence errors, overlapping scattering signals, undercounting and mis-sizing in highly concentrated seeding plumes. It also describes filtering measurements using a POPS baseline threshold. It does not establish ~10^4 cm^-3 as a general threshold at which errors begin: the counting limit varies with flow rate, and some high-concentration measurements appeared accurate. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe small quantity of seeding material (~20 g AgI per mission) represented a negligible addition relative to natural ice-nucleating particle concentrations, further limiting any lasting environmental impact.
“The small quantity of seeding material ( ∼ 20 g AgI per mission) represented a negligible addition relative to natural ice-nucleating particle concentrations, further limiting any lasting environmental impact (Fuchs et al., 2025; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Section 2.3 reports about 20 g of ice-active material per flare, but identifies it as part of a mixture rather than 20 g of AgI; the UAV can also burn two flares in a mission. Section 5.2 measures a seeding plume above background particle concentrations. The paper does not establish that the addition was negligible relative to natural ice-nucleating particles or assess lasting environmental impact. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe CLOUDLAB experiment aimed to advance understanding of ice crystal formation and growth dynamics in supercooled stratus clouds via targeted glaciogenic seeding using AgI particles delivered by UAVs.
“The CLOUDLAB experiment aimed to advance understanding of ice crystal formation and growth dynamics in supercooled stratus clouds via targeted glaciogenic seeding using AgI particles delivered by UAVs (Y. Chen et al., 2024; Fuchs et al., 2025; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Turbulent wind fluctuations introduced variability in plume dispersion, leading to lateral shifts of the seeding plume by 20-60 m from its expected trajectory in clear-sky calibration experiments.
“Turbulent wind fluctuations introduced variability in plume dispersion, leading to lateral shifts of the seeding plume by 20-60 m from its expected trajectory in clear-sky calibration experiments.”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
The POPS onboard the CLOUDLAB UAVs detected aerosol concentrations up to 15,000 cm^-3 in seeding plumes, while filtering data above baseline noise thresholds to ensure reliability.
“The POPS onboard the UAVs detected aerosol concentrations up to 15,000 cm -3 in seeding plumes, while filtering data above baseline noise thresholds to ensure reliability (Fuchs et al., 2025; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
These horizontal displacements, observed during transects flown by the measurement-collecting UAV, complicated plume tracking and highlighted challenges in predicting aerosol dispersion under real atmospheric turbulence.
“These horizontal displacements, observed during transects flown by the measurement-collecting UAV, complicated plume tracking and highlighted challenges in predicting aerosol dispersion under real atmospheric turbulence (Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
The CLOUDLAB UAVs were equipped with propeller heating systems to withstand icing in supercooled clouds and wind speeds up to 25 m s^-1.
“The UAVs were equipped with propeller heating systems to withstand icing in supercooled clouds and wind speeds up to 25 m s -1 (Fuchs et al., 2025; Miller et al., 2024)”
From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Miller et al., 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Kulkarni, P., Baron, P. A., & Willeke, K. (2011) · Wiley eBooks · doi:10.1002/9781118001684 · Cited by 1,363 · see citing works
Overview of Ice Nucleating Particles
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., & Krämer, M. (2017) · Meteorological Monographs · doi:10.1175/amsmonographs-d-16-0006.1 · Cited by 1,132 · see citing works
Self-citationChanges in the Onset of Spring in the Western United States
Cayan, D. R., Dettinger, M. D., Kammerdiener, S. A., Caprio, J. M., & Peterson, D. H. (2001) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(2001)082 · Cited by 906 · see citing works
Holmes, N. S., & Morawska, L. (2006) · Atmospheric Environment · doi:10.1016/j.atmosenv.2006.06.003 · Cited by 728 · see citing works
Seidel, D. J., Ao, C. O., & Li, K. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jd013680 · Cited by 713 · see citing works
The Nucleation of Ice Formation by Silver Iodide
Vonnegut, B. (1947) · Journal of Applied Physics · doi:10.1063/1.1697813 · Cited by 437 · see citing works
Mülmenstädt, J., Sourdeval, O., Delanoe͏̈, J., & Quaas, J. (2015) · Geophysical Research Letters · doi:10.1002/2015gl064604 · Cited by 389 · see citing works
Black carbon lofts wildfire smoke high into the stratosphere to form a persistent plume
Yu, P., Toon, O. B., Bardeen, C. G., Zhu, Y., Rosenlof, K. H., Portmann, R. W., Thornberry, T. D., Gao, R.-S., Davis, S. M., Wolf, E. T., et al. (2019) · Science · doi:10.1126/science.aax1748 · Cited by 363 · see citing works
The Production of Ice Crystals in a Cloud of Supercooled Water Droplets
Schaefer, V. J. (1946) · Science · doi:10.1126/science.104.2707.457 · Cited by 239 · see citing works
Fundamental aspects of modeling turbulent particle dispersion in dilute flows
Shirolkar, J. S., Coimbra, C. F. M., & McQuay, M. Q. (1996) · Progress in Energy and Combustion Science · doi:10.1016/s0360-1285(96)00006-8 · Cited by 237 · see citing works
Entekhabi, D., Asrar, G., Betts, A. K., Beven, K. J., Bras, R. L., Duffy, C. J., Dunne, T., Koster, R. D., Lettenmaier, D. P., McLaughlin, D. B., et al. (1999) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(1999)080 · Cited by 230 · see citing works
Yu, P., Rosenlof, K. H., Liu, S., Telg, H., Thornberry, T., Rollins, A. W., Portmann, R. W., Bai, Z., Ray, E., Duan, Y., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1701170114 · Cited by 193 · see citing works
A light-weight, high-sensitivity particle spectrometer for PM2.5 aerosol measurements
Gao, R. S., Telg, H., McLaughlin, R. J., Ciciora, S. J., Watts, L. A., Richardson, M. S., Schwarz, J. P., Perring, A. E., Thornberry, T., Rollins, A. W., et al. (2015) · Aerosol Science and Technology · doi:10.1080/02786826.2015.1131809 · Cited by 189 · see citing works
Unmanned aerial vehicle measurements of volcanic carbon dioxide fluxes
McGonigle, A. J. S., Aiuppa, A., Giudice, G., Tamburello, G., Hodson, A., & Gurrieri, S. (2008) · Geophysical Research Letters · doi:10.1029/2007gl032508 · Cited by 189 · see citing works
Coen, M. C., Praz, C., Haefele, A., Ruffieux, D., Kaufmann, P., & Calpini, B. (2014) · Atmospheric chemistry and physics · doi:10.5194/acp-14-13205-2014 · Cited by 174 · see citing works
Precipitation formation from orographic cloud seeding
French, J. R., Friedrich, K., Tessendorf, S. A., Rauber, R. M., Geerts, B., Rasmussen, R., Xue, L., Kunkel, M. L., & Blestrud, D. R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716995115 · Cited by 166 · see citing works
Heymsfield, A. J., Schmitt, C. G., Chen, C., Bansemer, A. R., Gettelman, A., Field, P. R., & Liu, C. (2020) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-19-0352.1 · Cited by 148 · see citing works
Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations
Bernstein, B. C., McDonough, F., Politovich, M. K., Brown, B. G., Ratvasky, T., Miller, D., Wolff, C. A., & Cunning, G. (2005) · Journal of applied meteorology · doi:10.1175/jam2246.1 · Cited by 147 · see citing works
The Small Unmanned Meteorological Observer SUMO: A new tool for atmospheric boundary layer research
Reuder, J., Brisset, P., Jonassen, M. M., & Mayer, S. (2009) · Meteorologische Zeitschrift · doi:10.1127/0941-2948/2009/0363 · Cited by 145 · see citing works
Knopf, D., & Alpert, P. A. (2023) · Nature Reviews Physics · doi:10.1038/s42254-023-00570-7 · Cited by 143 · see citing works
High-Fidelity Computational Aerodynamics of Multi-Rotor Unmanned Aerial Vehicles
Diaz, P. V., & Yoon, S. (2018) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 125 · see citing works
Simultaneous multicopter-based air sampling and sensing of meteorological variables
Brosy, C., Krampf, K., Zeeman, M., Wolf, B., Junkermann, W., Schäfer, K., Emeis, S. M., & Kunstmann, H. G. (2017) · Atmospheric measurement techniques · doi:10.5194/amt-10-2773-2017 · Cited by 118 · see citing works
Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations
Leuenberger, D., Haefele, A., Omanovic, N., Fengler, M. J., Martucci, G., Calpini, B., Fuhrer, O., & Rossa, A. (2020) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-19-0119.1 · Cited by 107 · see citing works
Self-citationFlossmann, A. I., & Wobrock, W. (2010) · Atmospheric Research · doi:10.1016/j.atmosres.2010.05.008 · Cited by 106 · see citing works
A key process controlling the wet removal of aerosols: new observational evidence
Ohata, S., Moteki, N., Mori, T., Koike, M., & Kondo, Y. (2016) · Scientific Reports · doi:10.1038/srep34113 · Cited by 99 · see citing works
Wintertime Orographic Cloud Seeding—A Review
Rauber, R. M., Geerts, B., Xue, L., French, J. R., Friedrich, K., Rasmussen, R., Tessendorf, S. A., Blestrud, D. R., Kunkel, M. L., & Parkinson, S. K. (2019) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-18-0341.1 · Cited by 96 · see citing works
de Boer, G., Ivey, M. D., Schmid, B., Lawrence, D., Dexheimer, D. N., Mei, F., Hubbe, J. M., Bendure, A., Hardesty, J. O. E., Shupe, M. D., et al. (2018) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-17-0156.1 · Cited by 89 · see citing works
The UAE Cloud Seeding Program: A Statistical and Physical Evaluation
Hosary, T. A., Mandous, A. A., Wehbe, Y., Shalaby, A., Alshamsi, N., Naqbi, H. A., Yazeedi, O. A., Mazroui, A. A., & Farrah, S. (2021) · Atmosphere · doi:10.3390/atmos12081013 · Cited by 88 · see citing works
Volcanic plume measurements using a UAV for the 2014 Mt. Ontake eruption
Mori, T., Hashimoto, T., Terada, A., Yoshimoto, M., Kazahaya, R., Shinohara, H., & Tanaka, R. (2016) · Earth Planets and Space · doi:10.1186/s40623-016-0418-0 · Cited by 85 · see citing works
Kezoudi, M., Keleshis, C., Antoniou, P., Biskos, G., Bronz, M., Constantinides, C., Desservettaz, M., Gao, R. S., Girdwood, J., Harnetiaux, J., et al. (2021) · Atmosphere · doi:10.3390/atmos12081042 · Cited by 83 · see citing works
Mamali, D., Marinou, E. D., Sciare, J., Pikridas, M., Kokkalis, P., Kottas, M., Binietoglou, I., Tsekeri, A., Keleshis, C., Engelmann, R., et al. (2018) · Atmospheric measurement techniques · doi:10.5194/amt-11-2897-2018 · Cited by 82 · see citing works
A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle
Molina, M. A., Gonzalez, F., Erskine, P. D., Cliff, D. I., & Heuff, D. (2017) · Sensors · doi:10.3390/s17020343 · Cited by 81 · see citing works
On the Use of Unmanned Aircraft for Sampling Mesoscale Phenomena in the Preconvective Boundary Layer
Koch, S. E., Fengler, M. J., Chilson, P. B., Elmore, K. L., Argrow, B., Andra, D. L., & Lindley, T. T. (2018) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-18-0101.1 · Cited by 70 · see citing works
Impact of the 2018 Ambae Eruption on the Global Stratospheric Aerosol Layer and Climate
Kloss, C., Sellitto, P., Legras, B., Vernier, J., Jégou, F., Ratnam, M. V., Kumar, B. S., Madhavan, B. L., & Berthet, G. (2020) · Journal of Geophysical Research Atmospheres · doi:10.1029/2020jd032410 · Cited by 68 · see citing works
Ramelli, F., Beck, A., Henneberger, J., & Lohmann, U. (2020) · Atmospheric measurement techniques · doi:10.5194/amt-13-925-2020 · Cited by 64 · see citing works
Self-citationAssessing the vertical structure of Arctic aerosols using balloon-borne measurements
Creamean, J. M., de Boer, G., Telg, H., Mei, F., Dexheimer, D. N., Shupe, M. D., Solomon, A. B., & McComiskey, A. C. (2021) · Atmospheric chemistry and physics · doi:10.5194/acp-21-1737-2021 · Cited by 64 · see citing works
UAV icing: Development of an ice protection system for the propeller of a small UAV
Müller, N. C., Løw-Hansen, B., Borup, K. T., & Hann, R. (2023) · Cold Regions Science and Technology · doi:10.1016/j.coldregions.2023.103938 · Cited by 54 · see citing works
A practical set of miniaturized instruments for vertical profiling of aerosol physical properties
Telg, H., Murphy, D. M., Bates, T. S., Johnson, J. E., Quinn, P. K., Giardi, F., & Gao, R. S. (2017) · Aerosol Science and Technology · doi:10.1080/02786826.2017.1296103 · Cited by 49 · see citing works
Järvi, L., Kurppa, M., Kuuluvainen, H., Rönkkö, T., Karttunen, S., Balling, A., Timonen, H., Niemi, J. V., & Pirjola, L. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158974 · Cited by 49 · see citing works
Samad, A., Florez, D. A., Chourdakis, I., & Vogt, U. (2022) · Atmosphere · doi:10.3390/atmos13050663 · Cited by 48 · see citing works
Henneberger, J., Ramelli, F., Spirig, R., Omanovic, N., Miller, A. J., Fuchs, C., Zhang, H., Bühl, J., Hervo, M., Kanji, Z. A., et al. (2023) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-22-0178.1 · Cited by 47 · see citing works
Self-citationInformation Content of Infrared Satellite Sounding Measurements with Respect to CO2
Engelen, R. J., & Stephens, G. L. (2004) · Journal of applied meteorology · doi:10.1175/1520-0450(2004)043 · Cited by 46 · see citing works
Performance Assessment of Portable Optical Particle Spectrometer (POPS)
Mei, F., McMeeking, G. R., Pekour, M., Gao, R. S., Kulkarni, G., China, S., Telg, H., Dexheimer, D. N., Tomlinson, J. M., & Schmid, B. (2020) · Sensors · doi:10.3390/s20216294 · Cited by 44 · see citing works
Rainfall enhancement in Karnataka state cloud seeding program “Varshadhare” 2017
Kulkarni, J. R., MORWAL, S. B., & Deshpande, N. R. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2018.12.020 · Cited by 35 · see citing works
Jozef, G., Cassano, J., Dahlke, S., & de Boer, G. (2022) · Atmospheric Measurement Techniques · doi:10.5194/amt-15-4001-2022 · Cited by 34 · see citing works
Thomas, D., & Charvet, A. (2017) · Elsevier eBooks · doi:10.1016/b978-1-78548-215-1.50001-9 · Cited by 34 · see citing works
Liu, Z., Osborne, M., Anderson, K., Shutler, J. D., Wilson, A., Langridge, J. M., Yim, S. H. L., Coe, H., Babu, S. S., Satheesh, S. K., et al. (2021) · Atmospheric measurement techniques · doi:10.5194/amt-14-6101-2021 · Cited by 32 · see citing works
Alaoui-Sosse, S., Durand, P., Medina, P., Pastor, P., Lothon, M., & Cernov, I. (2019) · Sensors · doi:10.3390/s19030581 · Cited by 31 · see citing works
The Israel 4 Cloud Seeding Experiment: Primary Results
Benjamini, Y., Givati, A., Khain, P., Levi, Y., Rosenfeld, D., Shamir, U. Y., Siegel, A., Zipori, A., Ziv, B., & Steinberg, D. M. (2023) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-22-0077.1 · Cited by 28 · see citing works
Evaluation of an Automatic Meteorological Drone Based on a 6-Month Measurement Campaign
Hervo, M., Romanens, G., Martucci, G., Weusthoff, T., & Haefele, A. (2023) · Atmosphere · doi:10.3390/atmos14091382 · Cited by 28 · see citing works
DeFelice, T. P., Axisa, D., Bird, J. J., Hirst, C. A., Frew, E. W., Burger, R. P., Baumgardner, D. G., Botha, G., Havenga, H., Breed, D., et al. (2023) · Atmospheric Research · doi:10.1016/j.atmosres.2023.106788 · Cited by 25 · see citing works
Drone-based meteorological observations up to the tropopause – a concept study
Bärfuss, K., Schmithüsen, H., & Lampert, A. (2023) · Atmospheric measurement techniques · doi:10.5194/amt-16-3739-2023 · Cited by 24 · see citing works
Jung, W., Cha, J. W., Ko, A.-R., Chae, S., Ro, Y., Hwang, H. J., Kim, B.-Y., Ku, J. M., Chang, K.-H., & Lee, C. (2022) · Advances in Meteorology · doi:10.1155/2022/3128657 · Cited by 23 · see citing works
Microphysical Characteristics and Evolution of Seeded Orographic Clouds
Friedrich, K., French, J. R., Tessendorf, S. A., Hatt, M., Weeks, C. E., Rauber, R. M., Geerts, B., Xue, L., Rasmussen, R., Blestrud, D. R., et al. (2021) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-20-0206.1 · Cited by 22 · see citing works
Fuertes, F. C., Wilhelm, L., & Porté‐Agel, F. (2019) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-17-0220.1 · Cited by 21 · see citing works
CAMP: an instrumented platform for balloon-borne aerosol particle studies in the lower atmosphere
Pilz, C., Düsing, S., Wehner, B., Müller, T., Siebert, H., Voigtländer, J., & Lonardi, M. (2022) · Atmospheric Measurement Techniques · doi:10.5194/amt-15-6889-2022 · Cited by 20 · see citing works
Lata, N. N., Cheng, Z., Dexheimer, D. N., Zhang, D., Mei, F., & China, S. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.2c09498 · Cited by 19 · see citing works
Observational data from uncrewed systems over Southern Great Plains
Mei, F., Pekour, M., Dexheimer, D. N., de Boer, G., Cook, R., Tomlinson, J. M., Schmid, B., Goldberger, L., Newsom, R., & Fast, J. D. (2022) · Earth system science data · doi:10.5194/essd-14-3423-2022 · Cited by 17 · see citing works
Pusfitasari, E. D., Ruiz‐Jiménez, J., Tiusanen, A., Suuronen, M., Haataja, J., Wu, Y., Kangasluoma, J., Luoma, K., Petäjä, T., Jussila, M., et al. (2023) · Atmospheric chemistry and physics · doi:10.5194/acp-23-5885-2023 · Cited by 17 · see citing works
Wang, W., Yao, Z., Guo, J., Tan, C., Jia, S., Zhao, W., Zhang, P., & Gao, L. (2019) · 气象学报：英文版 · doi:10.1007/s13351-019-8122-1 · Cited by 16 · see citing works
Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones
Suchanek, G., Wołoszyn, J., & Gołaś, A. (2022) · Sustainability · doi:10.3390/su14053049 · Cited by 13 · see citing works
Brus, D., Gustafsson, J., Kemppinen, O., de Boer, G., & Hirsikko, A. (2021) · Earth system science data · doi:10.5194/essd-13-2909-2021 · Cited by 12 · see citing works
Summa, D., Madonna, F., Franco, N., Rosa, B. D., & Girolamo, P. D. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-15-4153-2022 · Cited by 11 · see citing works
Albadra, A., Wood, K. T., Berthoud, L., Calway, A. D., Watson, M. I., Thomas, H. E., Richardson, T. S., Liu, E. J., & Chigna, G. (2019) · Journal of Volcanology and Geothermal Research · doi:10.1016/j.jvolgeores.2019.106731 · Cited by 11 · see citing works
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
Jin, L., Ghirardelli, M., Mann, J., Sjöholm, M., Kral, S. T., & Reuder, J. (2024) · Atmospheric measurement techniques · doi:10.5194/amt-17-2721-2024 · Cited by 9 · see citing works
Kasparoǧlu, S., Islam, M. M., Meskhidze, N., & Petters, M. D. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-15-5007-2022 · Cited by 9 · see citing works
Performance Analysis of Rotorcraft Propulsion Units in a Combination of Wind and Icing Conditions
Catry, G., Ceyhan, O., Noca, F., Bosson, N., Bardazzi, L., Márquez, S., Jordaens, P. J., & Brandolisio, D. (2021) · AIAA AVIATION Forum · doi:10.2514/6.2021-2677 · Cited by 8 · see citing works
Egerer, U., Cassano, J. J., Shupe, M. D., de Boer, G., Lawrence, D., Doddi, A., Siebert, H., Jozef, G. C., Calmer, R., Hamilton, J., et al. (2023) · Atmospheric measurement techniques · doi:10.5194/amt-16-2297-2023 · Cited by 7 · see citing works
Li, S., Xing, M., Jiang, L., Chen, P., Ding, F., & Yang, W. (2022) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2022.2134231 · Cited by 6 · see citing works
Mynard, A., Kent, J., Smith, E. R., Wilson, A., Wivell, K., Nelson, N., Hort, M., Bowles, J., Tiddeman, D., Langridge, J. M., et al. (2023) · Atmospheric measurement techniques · doi:10.5194/amt-16-4229-2023 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 71 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Griffith, D. A., Solak, M. E., and Yorty, D. P.: 30+ Winter Seasons Of Operational Cloud Seeding In Utah, Journal of Weather Mod- ification, 41, 23-37, https://journalofweathermodification.org/ index.php/JWM/article/view/175 (last access: 31 January 2022), 2009.
- Miller, A. J., Ramelli, F., Fuchs, C., Omanovic, N., Spirig, R., Huiy- ing, Z., Lohmann, U., Kanji, Z. A., and Henneberger, J.: Data for publication: "Two new multirotor Uncrewed Aerial Vehi- cles (UAVs) for glaciogenic cloud seeding and aerosol measure- ments within the CLOUDLAB project", ETH Zurich [data set], https://doi.org/20.500.11850/640942, 2023.
Cited by
Sorted by citations
Henneberger, J., Ramelli, F., Spirig, R., Omanovic, N., Miller, A. J., Fuchs, C., Zhang, H., Bühl, J., Hervo, M., Kanji, Z. A., et al. (2023) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-22-0178.1 · Cited by 47 · see citing works
Self-citationOmanovic, N., Ferrachat, S., Fuchs, C., Henneberger, J., Miller, A. J., Ohneiser, K., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., et al. (2024) · Atmospheric chemistry and physics · doi:10.5194/acp-24-6825-2024 · Cited by 20 · see citing works
Self-citationLi, Y., Zhang, C., Su, W., Jiang, S., Nie, D., Wang, Y., Wang, Y., He, H.-D., Chen, Q., Martin, S. T., et al. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c00074 · Cited by 18 · see citing works
Multirotor UAV icing correlated to liquid water content measurements in natural supercooled clouds
Miller, A. J., Fuchs, C., Omanovic, N., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., Fons, E., Lohmann, U., & Henneberger, J. (2024) · Cold Regions Science and Technology · doi:10.1016/j.coldregions.2024.104262 · Cited by 18 · see citing works
Self-citationQuantified ice-nucleating ability of AgI-containing seeding particles in natural clouds
Miller, A. J., Fuchs, C., Ramelli, F., Zhang, H., Omanovic, N., Spirig, R., Marcolli, C., Kanji, Z. A., Lohmann, U., & Henneberger, J. (2025) · Atmospheric chemistry and physics · doi:10.5194/acp-25-5387-2025 · Cited by 14 · see citing works
Self-citationRepurposing weather modification for cloud research showcased by ice crystal growth
Ramelli, F., Henneberger, J., Fuchs, C., Miller, A. J., Omanovic, N., Spirig, R., Zhang, H., David, R. O., Ohneiser, K., Seifert, P., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae402 · Cited by 12 · see citing works
Self-citationTarn, M. D., Shaw, K. J., Foster, P. B., West, J. S., Johnston, I. D., McCluskey, D. K., Peyman, S. A., & Murray, B. J. (2025) · Biomicrofluidics · doi:10.1063/5.0236911 · Cited by 9 · see citing works
Moormann, L., Böttger, T., Schuhmann, P., Valero, L., Fachinger, F., & Drewnick, F. (2025) · Atmospheric measurement techniques · doi:10.5194/amt-18-1441-2025 · Cited by 7 · see citing works
Kazim, M., Azzam, R., Burger, R. P., Wehbe, Y., Zweiri, Y., Seneviratne, L. D., & Werghi, N. (2025) · Atmospheric Research · doi:10.1016/j.atmosres.2025.108333 · Cited by 5 · see citing works
Kim, B.-Y., Belorid, M., Cha, J. W., Kim, Y., & Kim, S. (2025) · Atmospheric measurement techniques · doi:10.5194/amt-18-3781-2025 · Cited by 4 · see citing works
Quantifying ice crystal growth rates in natural clouds from glaciogenic cloud seeding experiments
Fuchs, C., Ramelli, F., Miller, A. J., Omanovic, N., Spirig, R., Zhang, H., Seifert, P., Ohneiser, K., Lohmann, U., & Henneberger, J. (2025) · Atmospheric Chemistry and Physics · doi:10.5194/acp-25-12177-2025 · Cited by 4 · see citing works
Self-citationInferring the controlling factors of ice aggregation from targeted cloud seeding experiments
Zhang, H., Ramelli, F., Fuchs, C., Omanovic, N., Miller, A. J., Spirig, R., Wu, Z., Chu, Y., Li, X., Lohmann, U., et al. (2026) · Atmospheric chemistry and physics · doi:10.5194/acp-26-1459-2026 · Cited by 4 · see citing works
Self-citationStepanian, P. M., & Williams, E. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-24-0109.1 · Cited by 4 · see citing works
Omanovic, N., Goger, B., & Lohmann, U. (2024) · Atmospheric Chemistry and Physics · doi:10.5194/acp-24-14145-2024 · Cited by 4 · see citing works
Self-citationOmanovic, N., Ferrachat, S., Fuchs, C., Ramelli, F., Henneberger, J., Miller, A. J., Spirig, R., Zhang, H., & Lohmann, U. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2025ms005016 · Cited by 3 · see citing works
Self-citationHernandez‐Galindo, I., Määttänen, A., Redmond Roche, B. H., Boucher, O., Irvine, P. J., & Moore, J. C. (2026) · Earth's Future · doi:10.1029/2025ef007254 · Cited by 2 · see citing works
Gaudek, T., Jiménez, C., Ohneiser, K., Fuchs, C., Henneberger, J., Bühl, J., Klamt, A., Ansmann, A., Engelmann, R., & Seifert, P. (2026) · Atmospheric measurement techniques · doi:10.5194/amt-19-2245-2026 · Cited by 2 · see citing works
Self-citationMiller, A. J., Fuchs, C., Omanovic, N., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., Lohmann, U., & Henneberger, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4789980
Self-citationGhebrezgiabher, G. A., & Gebreyesus, T. G. (2024) · IgMin Research · doi:10.61927/igmin248
Moormann, L. (2025) · doi:10.5194/egusphere-2024-3566-ac2
Comparative Analysis of the Landing Accuracy of the UAV for Capturing a Seismic Sensor
Aleksandr, S., Saveliev, A. I., & Cherskikh, E. (2025) · International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · doi:10.1109/summa68668.2025.11302354
Design and Experimental Evaluation of a Multirotor UAV Based on the Number of Propellers
Lakhdhar, L., Bouabidi, A., & Snoussi, A. (2025) · IEEE International Multi-Conference on Smart Systems & Green Process (IMC-SSGP) · doi:10.1109/imc-ssgp67001.2025.11474218
Zhang, H., Ong, C. R., Dipankar, A., Lohmann, U., & Henneberger, J. (2026) · Atmospheric chemistry and physics · doi:10.5194/acp-26-8677-2026
Self-citationCloud seeding and aerosol–cloud interactions: Environmental impacts and future perspectives
Shende, S., Gajbhiye, S. S., & Chaure, N. B. (2026) · Atmospheric Research · doi:10.1016/j.atmosres.2026.109223
Meteorological optoelectronic technology: from atmospheric observation to weather modification
Zhao, Z., Chen, Y., Chen, H., Li, J., Tang, Y., Li, J., Xu, H., Yang, D., Tang, T., Luo, L., et al. (2026) · Opto-Electronics Plus · doi:10.67704/oep.2026.260011
Aerosol-Focused Drone-Based Air Quality Monitoring for Regulatory Supplementation
Lange, M., Nehr, S., & Lampert, A. (2026) · Standards · doi:10.3390/standards6040041
- Authors
- Miller, A. J., Ramelli, F., Fuchs, C., Omanovic, N., Spirig, R., Zhang, H., Lohmann, U., Kanji, Z. A., & Henneberger, J.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Henneberger, Jan
- ORCID
- Venue
- Atmospheric Measurement Techniques
- Publisher
- Copernicus GmbH
- Published
- 2024-01-26
- Volume
- 17
- Issue
- 2
- Pages
- 601-625
- ISSN
- 1867-1381 (ISSN-L)
- 1867-8548
- OpenAlex
- W4391271762
- Cited by
- 26
- References
- 71
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 8 · 2023: 1
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Atmospheric measurement techniques (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- HORIZON EUROPE European Research Council (101021272 CLOUDLAB)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
- Keywords
- multirotor drones, supercooled clouds, aerosol-cloud interactions, ice nucleation, aerosol plumes, ice crystal number concentration, particle number size distribution, CLOUDLAB, flight patterns, data quality filtering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
