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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 7
Atmos. Meas. Tech., 11, 4261-4272, 2018
https://doi.org/10.5194/amt-11-4261-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 4261-4272, 2018
https://doi.org/10.5194/amt-11-4261-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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. Larsen1,2 and Raymond A. Shaw2 Michael L. Larsen and Raymond A. Shaw
  • 1Department of Physics and Astronomy, College of Charleston, Charleston, SC, USA
  • 2Department of Physics, Michigan Technological University, Houghton, MI, USA

Abstract. Reliable measurements of the three-dimensional radial distribution function for cloud droplets are desired to help characterize microphysical processes that depend on local drop environment. Existing numerical techniques to estimate this three-dimensional radial distribution function are not well suited to in situ or laboratory data gathered from a finite experimental domain. This paper introduces and tests a new method designed to reliably estimate the three-dimensional radial distribution function in contexts in which (i) physical considerations prohibit the use of periodic boundary conditions and (ii) particle positions are measured inside a convex volume that may have a large aspect ratio. The method is then utilized to measure the three-dimensional radial distribution function from laboratory data taken in a cloud chamber from the Holographic Detector for Clouds (HOLODEC).

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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.
A statistical tool frequently utilized to measure scale-dependent departures from perfect...
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