Articles | Volume 11, issue 7
Atmos. Meas. Tech., 11, 4261–4272, 2018
https://doi.org/10.5194/amt-11-4261-2018
Atmos. Meas. Tech., 11, 4261–4272, 2018
https://doi.org/10.5194/amt-11-4261-2018
Research article
19 Jul 2018
Research article | 19 Jul 2018

A method for computing the three-dimensional radial distribution function of cloud particles from holographic images

Michael L. Larsen and Raymond A. Shaw

Related authors

Effects of the large-scale circulation on temperature and water vapor distributions in the Π Chamber
Jesse C. Anderson, Subin Thomas, Prasanth Prabhakaran, Raymond A. Shaw, and Will Cantrell
Atmos. Meas. Tech., 14, 5473–5485, https://doi.org/10.5194/amt-14-5473-2021,https://doi.org/10.5194/amt-14-5473-2021, 2021
Short summary
Characterization and first results from LACIS-T: a moist-air wind tunnel to study aerosol–cloud–turbulence interactions
Dennis Niedermeier, Jens Voigtländer, Silvio Schmalfuß, Daniel Busch, Jörg Schumacher, Raymond A. Shaw, and Frank Stratmann
Atmos. Meas. Tech., 13, 2015–2033, https://doi.org/10.5194/amt-13-2015-2020,https://doi.org/10.5194/amt-13-2015-2020, 2020
Short summary
Turbulence-induced cloud voids: observation and interpretation
Katarzyna Karpińska, Jonathan F. E. Bodenschatz, Szymon P. Malinowski, Jakub L. Nowak, Steffen Risius, Tina Schmeissner, Raymond A. Shaw, Holger Siebert, Hengdong Xi, Haitao Xu, and Eberhard Bodenschatz
Atmos. Chem. Phys., 19, 4991–5003, https://doi.org/10.5194/acp-19-4991-2019,https://doi.org/10.5194/acp-19-4991-2019, 2019
Short summary
Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation
Fan Yang, Pavlos Kollias, Raymond A. Shaw, and Andrew M. Vogelmann
Atmos. Chem. Phys., 18, 7313–7328, https://doi.org/10.5194/acp-18-7313-2018,https://doi.org/10.5194/acp-18-7313-2018, 2018
Short summary
Conditions for super-adiabatic droplet growth after entrainment mixing
Fan Yang, Raymond Shaw, and Huiwen Xue
Atmos. Chem. Phys., 16, 9421–9433, https://doi.org/10.5194/acp-16-9421-2016,https://doi.org/10.5194/acp-16-9421-2016, 2016
Short summary

Related subject area

Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021,https://doi.org/10.5194/amt-14-7079-2021, 2021
Short summary
Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning
Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
Atmos. Meas. Tech., 14, 6695–6710, https://doi.org/10.5194/amt-14-6695-2021,https://doi.org/10.5194/amt-14-6695-2021, 2021
Short summary
Application of cloud particle sensor sondes for estimating the number concentration of cloud water droplets and liquid water content: case studies in the Arctic region
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021,https://doi.org/10.5194/amt-14-4971-2021, 2021
Short summary
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020,https://doi.org/10.5194/amt-13-5595-2020, 2020
Short summary
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020,https://doi.org/10.5194/amt-13-2219-2020, 2020
Short summary

Cited articles

Ayala, O., Rosa, B., Wang, L.-P., and Grabowski, W.: Effects of turbulence on the geometric collision rate of sedimenting droplets. Part I: Results from direct numerical simulation, New J. Phys., 10, 075015, https://doi.org/10.1088/1367-2630/10/7/075015, 2008.
Baker, B.: Turbulent entrainment and mixing in clouds: A new observational approach, J. Atmos. Sci., 49, 387–404, 1992.
Baker, B. and Lawson, R.: Analysis of tools used to quantify droplet clustering in clouds, J. Atmos. Sci., 67, 3355–3367, 2010.
Balkovsky, E., Falkovich, G., and Fouxon, A.: Intermittent distribution of inertial particles in turbulent flows, Phys. Rev. Lett., 86, 2790–2793, 2001.
Bateson, C. and Aliseda, A.: Wind tunnel measurements of the preferential concentration of inertial droplets in homogenous isotropic turbulence, Exp. Fluids, 52, 1373–1387, 2012.
Download
Short summary
A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.