Articles | Volume 22, issue 9
https://doi.org/10.5194/acp-22-6197-2022
© 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-22-6197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site
Russell J. Perkins
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Peter J. Marinescu
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA
Ezra J. T. Levin
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Handix Scientific, Fort Collins, CO 80525, USA
Don R. Collins
Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, CA 92521, USA
Sonia M. Kreidenweis
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
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Charles M. Davis, Susan C. van den Heever, Leah D. Grant, Sonia M. Kreidenweis, Claudia Mignani, Russell J. Perkins, and Elizabeth A. Stone
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Plant- and other biological matter is released into the air from the earth’s surface when it rains. When present in clouds, these particles promote ice formation. We simulate three kinds of storms to see whether they pick up surface air from rainy regions where these particles would be. We find that all the storms ingest similar amounts of air from regions of light rain, but the types of storms that are typically longer-lived and more severe ingest more air from regions of heavy rain.
Ryan J. Patnaude, Kathryn A. Moore, Russell J. Perkins, Thomas C. J. Hill, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 24, 911–928, https://doi.org/10.5194/acp-24-911-2024, https://doi.org/10.5194/acp-24-911-2024, 2024
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In this study we examined the effect of atmospheric aging on sea spray aerosols (SSAs) to form ice and how newly formed secondary marine aerosols (SMAs) may freeze at cirrus temperatures (< −38 °C). Results show that SSAs freeze at different relative humidities (RHs) depending on the temperature and that the ice-nucleating ability of SSA was not hindered by atmospheric aging. SMAs are shown to freeze at high RHs and are likely inefficient at forming ice at cirrus temperatures.
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Plant- and other biological matter is released into the air from the earth’s surface when it rains. When present in clouds, these particles promote ice formation. We simulate three kinds of storms to see whether they pick up surface air from rainy regions where these particles would be. We find that all the storms ingest similar amounts of air from regions of light rain, but the types of storms that are typically longer-lived and more severe ingest more air from regions of heavy rain.
Aino Ovaska, Elio Rauth, Daniel Holmberg, Paulo Artaxo, John Backman, Benjamin Bergmans, Don Collins, Marco Aurélio Franco, Shahzad Gani, Roy M. Harrison, Rakes K. Hooda, Tareq Hussein, Antti-Pekka Hyvärinen, Kerneels Jaars, Adam Kristensson, Markku Kulmala, Lauri Laakso, Ari Laaksonen, Nikolaos Mihalopoulos, Colin O'Dowd, Jakub Ondracek, Tuukka Petäjä, Kristina Plauškaitė, Mira Pöhlker, Ximeng Qi, Peter Tunved, Ville Vakkari, Alfred Wiedensohler, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen
Aerosol Research Discuss., https://doi.org/10.5194/ar-2025-18, https://doi.org/10.5194/ar-2025-18, 2025
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We trained machine learning models to estimate the number of aerosol particles large enough to form clouds and generated daily estimates for the entire globe. The models performed well in many continental regions but struggled in remote and marine areas. Still, this approach offers a way to quantify these particles in areas that lack direct measurements, helping us understand their influence on clouds and climate on a global scale.
Leah D. Gibson, Ezra J. T. Levin, Ethan Emerson, Nick Good, Anna Hodshire, Gavin McMeeking, Kate Patterson, Bryan Rainwater, Tom Ramin, and Ben Swanson
Atmos. Chem. Phys., 25, 2745–2762, https://doi.org/10.5194/acp-25-2745-2025, https://doi.org/10.5194/acp-25-2745-2025, 2025
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From fall 2021 to summer 2023, SAIL-Net, a network of six aerosol measurement nodes, was deployed in the East River watershed (Colorado, USA) to study aerosol variability across space and time in mountainous terrain. We found that aerosol variability is influenced by elevation differences, with the most representative site in the region changing seasonally, suggesting aerosol spatial variability also varies seasonally. This work offers a blueprint for future studies in other mountainous regions.
Kevin R. Barry, Thomas C. J. Hill, Sonia M. Kreidenweis, Paul J. DeMott, Yutaka Tobo, and Jessie M. Creamean
EGUsphere, https://doi.org/10.5194/egusphere-2025-128, https://doi.org/10.5194/egusphere-2025-128, 2025
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The Arctic is changing rapidly, and we sought to better understand how their clouds may change in the future through quantifying the natural cloud seeding particles over a year and uncover what they are made of. We wanted to determine their likely sources through concurrent DNA sequencing of airborne bacteria and fungi and found a persistent mixture of local and longer-range sources at all times.
Sean W. Freeman, Jennie Bukowski, Leah D. Grant, Peter J. Marinescu, J. Minnie Park, Stacey M. Hitchcock, Christine A. Neumaier, and Susan C. van den Heever
EGUsphere, https://doi.org/10.5194/egusphere-2024-2425, https://doi.org/10.5194/egusphere-2024-2425, 2025
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In this work, we tested different placements of a temperature and humidity sensor onboard a drone to understand what the relative errors are. Understanding these errors is critical as we want to collect more meteorological data from non-specialized platforms, such as drone swarms and drone package delivery.
Ningjin Xu, Chen Le, David R. Cocker, Kunpeng Chen, Ying-Hsuan Lin, and Don R. Collins
Atmos. Meas. Tech., 17, 4227–4243, https://doi.org/10.5194/amt-17-4227-2024, https://doi.org/10.5194/amt-17-4227-2024, 2024
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A flow-through reactor was developed that exposes known mixtures of gases or ambient air to very high concentrations of the oxidants that are responsible for much of the chemistry that takes place in the atmosphere. Like other reactors of its type, it is primarily used to study the formation of particulate matter from the oxidation of common gases. Unlike other reactors of its type, it can simulate the chemical reactions that occur in liquid water that is present in particles or cloud droplets.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Zihan Zhu, Javier González-Rocha, Yifan Ding, Isis Frausto-Vicencio, Sajjan Heerah, Akula Venkatram, Manvendra Dubey, Don Collins, and Francesca M. Hopkins
Atmos. Meas. Tech., 17, 3883–3895, https://doi.org/10.5194/amt-17-3883-2024, https://doi.org/10.5194/amt-17-3883-2024, 2024
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Increases in agriculture, oil and gas, and waste management activities have contributed to the increase in atmospheric methane levels and resultant climate warming. In this paper, we explore the use of small uncrewed aircraft systems (sUASs) and AirCore technology to detect and quantify methane emissions. Results from field experiments demonstrate that sUASs and AirCore technology can be effective for detecting and quantifying methane emissions in near real time.
Ryan J. Patnaude, Kathryn A. Moore, Russell J. Perkins, Thomas C. J. Hill, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 24, 911–928, https://doi.org/10.5194/acp-24-911-2024, https://doi.org/10.5194/acp-24-911-2024, 2024
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In this study we examined the effect of atmospheric aging on sea spray aerosols (SSAs) to form ice and how newly formed secondary marine aerosols (SMAs) may freeze at cirrus temperatures (< −38 °C). Results show that SSAs freeze at different relative humidities (RHs) depending on the temperature and that the ice-nucleating ability of SSA was not hindered by atmospheric aging. SMAs are shown to freeze at high RHs and are likely inefficient at forming ice at cirrus temperatures.
Kevin R. Barry, Thomas C. J. Hill, Marina Nieto-Caballero, Thomas A. Douglas, Sonia M. Kreidenweis, Paul J. DeMott, and Jessie M. Creamean
Atmos. Chem. Phys., 23, 15783–15793, https://doi.org/10.5194/acp-23-15783-2023, https://doi.org/10.5194/acp-23-15783-2023, 2023
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Ice-nucleating particles (INPs) are important for the climate due to their influence on cloud properties. To understand potential land-based sources of them in the Arctic, we carried out a survey near the northernmost point of Alaska, a landscape connected to the permafrost (thermokarst). Permafrost contained high concentrations of INPs, with the largest values near the coast. The thermokarst lakes were found to emit INPs, and the water contained elevated concentrations.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
Atmos. Chem. Phys., 22, 12803–12825, https://doi.org/10.5194/acp-22-12803-2022, https://doi.org/10.5194/acp-22-12803-2022, 2022
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The evolution of organic aerosol composition and size is uncertain due to variability within and between smoke plumes. We examine the impact of plume concentration on smoke evolution from smoke plumes sampled by the NASA DC-8 during FIREX-AQ. We find that observed organic aerosol and size distribution changes are correlated to plume aerosol mass concentrations. Additionally, coagulation explains the majority of the observed growth.
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
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This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-216, https://doi.org/10.5194/amt-2022-216, 2022
Publication in AMT not foreseen
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The new Continuous Flow Diffusion Chamber-Ice Activation Spectrometer collected 4 months of ice nucleating particle (INP) measurements at a 5-minute resolution at the mountainside Storm Peak Laboratory. Most long-term INP measurements are at a time resolution of a day or longer: our instrument is a promising advance towards high-resolution long-term INP measurements. We observe higher peak INP concentrations than previous mountain studies, possibly due to the higher time resolution of our data.
Lu Chen, Fang Zhang, Don Collins, Jingye Ren, Jieyao Liu, Sihui Jiang, and Zhanqing Li
Atmos. Chem. Phys., 22, 2293–2307, https://doi.org/10.5194/acp-22-2293-2022, https://doi.org/10.5194/acp-22-2293-2022, 2022
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Understanding the volatility and mixing state of atmospheric aerosols is important for elucidating their formation. Here, the size-resolved volatility of fine particles is characterized using field measurements. On average, the particles are more volatile in the summer. The retrieved mixing state shows that black carbon (BC)-containing particles dominate and contribute 67–77 % toward the total number concentration in the winter, while the non-BC particles accounted for 52–69 % in the summer.
Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-29, https://doi.org/10.5194/acp-2022-29, 2022
Preprint withdrawn
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The new Continuous Flow Diffusion Chamber-Ice Activation Spectrometer collected 4 months of ice nucleating particle (INP) measurements at a 5-minute resolution at the mountainside Storm Peak Laboratory. Most long-term INP measurements are at a time resolution of a day or longer: our instrument is a promising advance towards high-resolution long-term INP measurements. We observe higher peak INP concentrations than previous mountain studies, possibly due to the higher time resolution of our data.
Candice L. Sirmollo, Don R. Collins, Jordan M. McCormick, Cassandra F. Milan, Matthew H. Erickson, James H. Flynn, Rebecca J. Sheesley, Sascha Usenko, Henry W. Wallace, Alexander A. T. Bui, Robert J. Griffin, Matthew Tezak, Sean M. Kinahan, and Joshua L. Santarpia
Atmos. Meas. Tech., 14, 3351–3370, https://doi.org/10.5194/amt-14-3351-2021, https://doi.org/10.5194/amt-14-3351-2021, 2021
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The newly developed portable 1 m3 CAGE chamber systems were characterized using data acquired during a 2-month field study in 2016 in a forested area north of Houston, TX, USA. Concentrations of several oxidant and organic compounds measured in the chamber were found to closely agree with those calculated with a zero-dimensional model. By tracking the modes of injected monodisperse particles, a pattern change was observed for hourly averaged growth rates between late summer and early fall.
Anna L. Hodshire, Emily Ramnarine, Ali Akherati, Matthew L. Alvarado, Delphine K. Farmer, Shantanu H. Jathar, Sonia M. Kreidenweis, Chantelle R. Lonsdale, Timothy B. Onasch, Stephen R. Springston, Jian Wang, Yang Wang, Lawrence I. Kleinman, Arthur J. Sedlacek III, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 6839–6855, https://doi.org/10.5194/acp-21-6839-2021, https://doi.org/10.5194/acp-21-6839-2021, 2021
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Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
Ningjin Xu and Don R. Collins
Atmos. Meas. Tech., 14, 2891–2906, https://doi.org/10.5194/amt-14-2891-2021, https://doi.org/10.5194/amt-14-2891-2021, 2021
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Oxidation flow reactors (OFRs) are frequently used to study atmospheric chemistry and aerosol formation by accelerating by up to 10 000 times the reactions that can take hours, days, or even weeks in the atmosphere. Here we present the design and evaluation of a new all-Teflon OFR. The computational, laboratory, and field use data we present demonstrate that the PFA OFR is suitable for a range of applications, including the study of rapidly changing ambient concentrations.
Cited articles
Alexandrov, M. D., Marshak, A., Cairns, B., Lacis, A. A., and Carlson, B.
E.: Scaling Properties of Aerosol Optical Thickness Retrieved from
Ground-Based Measurements, J. Atmos. Sci., 61, 1024–1039,
https://doi.org/10.1175/1520-0469(2004)061<1024:SPOAOT>2.0.CO;2, 2004.
Alexandrov, M. D., Geogdzhayev, I. V., Tsigaridis, K., Marshak, A., Levy,
R., and Cairns, B.: New Statistical Model for Variability of Aerosol Optical
Thickness: Theory and Application to MODIS Data over Ocean, J. Atmos.
Sci., 73, 821–837, https://doi.org/10.1175/JAS-D-15-0130.1, 2016.
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and
Holmén, K.: Mesoscale Variations of Tropospheric Aerosols, J.
Atmos. Sci., 60, 119–136,
https://doi.org/10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003.
Bianchi, F., Tröstl, J., Junninen, H., Frege, C., Henne, S., Hoyle, C.
R., Molteni, U., Herrmann, E., Adamov, A., Bukowiecki, N., Chen, X.,
Duplissy, J., Gysel, M., Hutterli, M., Kangasluoma, J., Kontkanen, J.,
Kürten, A., Manninen, H. E., Münch, S., Peräkylä, O.,
Petäjä, T., Rondo, L., Williamson, C., Weingartner, E., Curtius, J.,
Worsnop, D. R., Kulmala, M., Dommen, J., and Baltensperger, U.: New particle
formation in the free troposphere: A question of chemistry and timing,
Science, 352, 11091112, https://doi.org/10.1126/science.aad5456, 2016.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time Series
Analysis: Forecasting and Control, 5th Edition, John Wiley and Sons Inc.,
Hoboken, New Jersey, 712 pp., ISBN 978-1-118-67502-1, 2015.
Carrico, C. M., Petters, M. D., Kreidenweis, S. M., Sullivan, A. P., McMeeking, G. R., Levin, E. J. T., Engling, G., Malm, W. C., and Collett Jr., J. L.: Water uptake and chemical composition of fresh aerosols generated in open burning of biomass, Atmos. Chem. Phys., 10, 5165–5178, https://doi.org/10.5194/acp-10-5165-2010, 2010.
Cheng, W. Y. Y., Carrió, G. G., Cotton, W. R., and Saleeby, S. M.:
Influence of cloud condensation and giant cloud condensation nuclei on the
development of precipitating trade wind cumuli in a large eddy simulation,
J. Geophys. Res.-Atmos., 114, D08201, https://doi.org/10.1029/2008JD011011,
2009.
Cohard, J.-M., Pinty, J.-P., and Bedos, C.: Extending Twomey's Analytical
Estimate of Nucleated Cloud Droplet Concentrations from CCN Spectra, J. Atmos. Sci., 55, 3348–3357,
https://doi.org/10.1175/1520-0469(1998)055<3348:ETSAEO>2.0.CO;2, 1998.
Collins, D.: ARM: Tandem Differential Mobility Analyzer: size-resolved
concentrations, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1025303, 2005.
Collins, D.: ARM: Tandem Differential Mobility Analyzer Aerosol Particle
Sizer, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1150275, 2010a.
Collins, D.: Tandem Differential Mobility Analyzer/Aerodynamic Particle
Sizer (APS) Handbook, PNNL, Richland, WA, https://doi.org/10.2172/982072,
2010b.
DeCarlo, P. F., Slowik, J. G., Worsnop, D. R., Davidovits, P., and Jimenez,
J. L.: Particle Morphology and Density Characterization by Combined Mobility
and Aerodynamic Diameter Measurements. Part 1: Theory, Aerosol Sci.
Tech., 38, 1185–1205, https://doi.org/10.1080/027868290903907, 2004.
Feingold, G., Cotton, W. R., Kreidenweis, S. M., and Davis, J. T.: The
Impact of Giant Cloud Condensation Nuclei on Drizzle Formation in
Stratocumulus: Implications for Cloud Radiative Properties, J. Atmos. Sci., 56, 4100–4117, https://doi.org/10.1175/1520-0469(1999)056<4100:TIOGCC>2.0.CO;2, 1999.
Gantt, B., He, J., Zhang, X., Zhang, Y., and Nenes, A.: Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects, Atmos. Chem. Phys., 14, 7485–7497, https://doi.org/10.5194/acp-14-7485-2014, 2014.
Gerber, H.: Supersaturation and Droplet Spectral Evolution in Fog, J. Atmos. Sci., 48, 2569–2588,
https://doi.org/10.1175/1520-0469(1991)048<2569:SADSEI>2.0.CO;2, 1991.
Glenn, I. B., Feingold, G., Gristey, J. J., and Yamaguchi, T.:
Quantification of the Radiative Effect of Aerosol–Cloud Interactions in
Shallow Continental Cumulus Clouds, J. Atmos. Sci., 77, 2905–2920,
https://doi.org/10.1175/JAS-D-19-0269.1, 2020.
Hageman, D., Behrens, B., Smith, S., Uin, J., Salwen, C., Koontz, A.,
Jefferson, A., Watson, T., Sedlacek, A., Kuang, C., Dubey, M., Springston,
S., and Senum, G.: ARM: Aerosol Observing System (AOS): aerosol data, 1-min,
mentor-QC applied, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1025259, 1996.
Hodshire, A. L., Lawler, M. J., Zhao, J., Ortega, J., Jen, C., Yli-Juuti, T., Brewer, J. F., Kodros, J. K., Barsanti, K. C., Hanson, D. R., McMurry, P. H., Smith, J. N., and Pierce, J. R.: Multiple new-particle growth pathways observed at the US DOE Southern Great Plains field site, Atmos. Chem. Phys., 16, 9321–9348, https://doi.org/10.5194/acp-16-9321-2016, 2016.
Hudson, J. G., Jha, V., and Noble, S.: Drizzle correlations with giant
nuclei, Geophys. Res. Lett., 38, L05808, https://doi.org/10.1029/2010GL046207, 2011.
Johnson, D. B.: The Role of Giant and Ultragiant Aerosol Particles in Warm
Rain Initiation, J. Atmos. Sci., 39, 448–460,
https://doi.org/10.1175/1520-0469(1982)039<0448:TROGAU>2.0.CO;2, 1982.
Jung, E., Albrecht, B. A., Jonsson, H. H., Chen, Y.-C., Seinfeld, J. H., Sorooshian, A., Metcalf, A. R., Song, S., Fang, M., and Russell, L. M.: Precipitation effects of giant cloud condensation nuclei artificially introduced into stratocumulus clouds, Atmos. Chem. Phys., 15, 5645–5658, https://doi.org/10.5194/acp-15-5645-2015, 2015.
Koontz, A., Flynn, C., Uin, J., and Jefferson, A.: AOS humidified
nephelometer, harmonized, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1228051, 2012.
Levin, Z. and Cotton, W. R. (Eds.): Aerosol Pollution Impact on
Precipitation: A Scientific Review, Springer Netherlands,
https://doi.org/10.1007/978-1-4020-8690-8, 2009.
Low, R. D. H.: Microphysical and meteorological measurements of fog
supersaturation, Tellus, 27, 507–513,
https://doi.org/10.3402/tellusa.v27i5.10177, 1975.
Mahish, M. and Collins, D.: Analysis of a Multi-Year Record of Size-Resolved
Hygroscopicity Measurements from a Rural Site in the U.S., Aerosol Air Qual.
Res., 17, 1489–1500, https://doi.org/10.4209/aaqr.2016.10.0443, 2017.
Marinescu, P. and Levin, E.: SGP Merged Aerosol Size Distribution
(CPC+SMPS+APS), Atmospheric Radiation Measurement (ARM) Archive [data set], United
States, https://doi.org/10.5439/1511037, 2019.
Marinescu, P. J., Heever, S. C. van den, Saleeby, S. M., Kreidenweis, S. M.,
and DeMott, P. J.: The Microphysical Roles of Lower-Tropospheric versus
Midtropospheric Aerosol Particles in Mature-Stage MCS Precipitation, J. Atmos. Sci., 74, 3657–3678, https://doi.org/10.1175/JAS-D-16-0361.1,
2017.
Marinescu, P. J., Levin, E. J. T., Collins, D., Kreidenweis, S. M., and van den Heever, S. C.: Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA, Atmos. Chem. Phys., 19, 11985–12006, https://doi.org/10.5194/acp-19-11985-2019, 2019.
Nieminen, T., Kerminen, V.-M., Petäjä, T., Aalto, P. P., Arshinov, M., Asmi, E., Baltensperger, U., Beddows, D. C. S., Beukes, J. P., Collins, D., Ding, A., Harrison, R. M., Henzing, B., Hooda, R., Hu, M., Hõrrak, U., Kivekäs, N., Komsaare, K., Krejci, R., Kristensson, A., Laakso, L., Laaksonen, A., Leaitch, W. R., Lihavainen, H., Mihalopoulos, N., Németh, Z., Nie, W., O'Dowd, C., Salma, I., Sellegri, K., Svenningsson, B., Swietlicki, E., Tunved, P., Ulevicius, V., Vakkari, V., Vana, M., Wiedensohler, A., Wu, Z., Virtanen, A., and Kulmala, M.: Global analysis of continental boundary layer new particle formation based on long-term measurements, Atmos. Chem. Phys., 18, 14737–14756, https://doi.org/10.5194/acp-18-14737-2018, 2018.
Patel, P. N. and Jiang, J. H.: Cloud condensation nuclei characteristics at
the Southern Great Plains site: role of particle size distribution and
aerosol hygroscopicity, Environ. Res. Commun., 3, 075002,
https://doi.org/10.1088/2515-7620/ac0e0b, 2021.
Perkins, R.: Southern Great Plains Merged and Extended Cloud Condensation
Nuclei Data, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1832908, 2009.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Pierce, J. R., Westervelt, D. M., Atwood, S. A., Barnes, E. A., and Leaitch, W. R.: New-particle formation, growth and climate-relevant particle production in Egbert, Canada: analysis from 1 year of size-distribution observations, Atmos. Chem. Phys., 14, 8647–8663, https://doi.org/10.5194/acp-14-8647-2014, 2014.
Pinsky, M., Khain, A., Mazin, I., and Korolev, A.: Analytical estimation of
droplet concentration at cloud base, J. Geophys. Res.-Atmos., 117, D18211,
https://doi.org/10.1029/2012JD017753, 2012.
Posselt, R. and Lohmann, U.: Influence of Giant CCN on warm rain processes in the ECHAM5 GCM, Atmos. Chem. Phys., 8, 3769–3788, https://doi.org/10.5194/acp-8-3769-2008, 2008.
Saleeby, S. M., van den Heever, S. C., Marinescu, P. J., Kreidenweis, S. M.,
and DeMott, P. J.: Aerosol effects on the anvil characteristics of mesoscale
convective systems, J. Geophys. Res.-Atmos., 121, 10880–10901,
https://doi.org/10.1002/2016JD025082, 2016.
Salwen, C., Boyer, M., Springston, S., Kuang, C., and Andrews, E.: ARM: AOS:
condensation particle counter, Atmospheric Radiation Measurement (ARM)
Archive [data set], https://doi.org/10.5439/1025152, 1990.
Sayer, A. M. and Knobelspiesse, K. D.: How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth, Atmos. Chem. Phys., 19, 15023–15048, https://doi.org/10.5194/acp-19-15023-2019, 2019.
Shen, C., Zhao, C., Ma, N., Tao, J., Zhao, G., Yu, Y., and Kuang, Y.: Method
to Estimate Water Vapor Supersaturation in the Ambient Activation Process
Using Aerosol and Droplet Measurement Data, J. Geophys. Res.-Atmos.,
123, 10606–10619, https://doi.org/10.1029/2018JD028315, 2018.
Tibshirani, R., Walther, G., and Hastie, T.: Estimating the number of
clusters in a data set via the gap statistic, J. R. Stat. Soc. B, 63, 411–423, https://doi.org/10.1111/1467-9868.00293, 2001.
Uin, J.: Cloud Condensation Nuclei Particle Counter Instrument Handbook, DOE
ARM Clim. Res. Facil., https://doi.org/10.2172/1251411, 2016.
Venzac, H., Sellegri, K., Laj, P., Villani, P., Bonasoni, P., Marinoni, A.,
Cristofanelli, P., Calzolari, F., Fuzzi, S., Decesari, S., Facchini, M.-C.,
Vuillermoz, E., and Verza, G. P.: High frequency new particle formation in
the Himalayas, P. Natl. Acad. Sci. USA, 105, 15666–15671,
https://doi.org/10.1073/pnas.0801355105, 2008.
Zawadowicz, M. and Howie, J.: Aerosol Chemical Speciation Monitor, mentor
processed, .c2, Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1763029, 2021.
Short summary
We used 5 years (2009–2013) of aerosol and cloud condensation nuclei (CCN) data from a total of seven instruments housed at the Southern Great Plains site, which were merged into a quality-controlled, continuous dataset of CCN spectra at ~45 min resolution. The data cover all seasons, are representative of a rural, agricultural mid-continental site, and are useful for model initialization and validation. Our analysis of this dataset focuses on seasonal and hourly variability.
We used 5 years (2009–2013) of aerosol and cloud condensation nuclei (CCN) data from a total of...
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