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Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance StatementA typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.
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Holographic Observations of Centimeter-Scale Nonuniformities within Marine Stratocumulus Clouds
Abstract Data collected with a holographic instrument [Holographic Detector for Clouds (HOLODEC)] on board the High-Performance Instrumented Airborne Platform for Environmental Research Gulfstream-V (HIAPER GV) aircraft from marine stratocumulus clouds during the Cloud System Evolution in the Trades (CSET) field project are examined for spatial uniformity. During one flight leg at 1190 m altitude, 1816 consecutive holograms were taken, which were approximately 40 m apart with individual hologram dimensions of 1.16 cm × 0.68 cm × 12.0 cm and with droplet concentrations of up to 500 cm−3. Unlike earlier studies, minimally intrusive data processing (e.g., bypassing calculation of number concentrations, binning, and parametric fitting) is used to test for spatial uniformity of clouds on intra- and interhologram spatial scales (a few centimeters and 40 m, respectively). As a means to test this, measured droplet count fluctuations are normalized with the expected standard deviation from theoretical Poisson distributions, which signifies randomness. Despite the absence of trends in the mean concentration, it is found that the null hypothesis of spatial uniformity on both spatial scales can be rejected with compelling statistical confidence. Monte Carlo simulations suggest that weak clustering explains this signature. These findings also hold for size-resolved analysis but with less certainty. Clustering of droplets caused by, for example, entrainment and turbulence, is size dependent and is likely to influence key processes such as droplet growth and thus cloud lifetime.
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- PAR ID:
- 10131704
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of the Atmospheric Sciences
- Volume:
- 77
- Issue:
- 2
- ISSN:
- 0022-4928
- Page Range / eLocation ID:
- p. 499-512
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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