Capsule summary.
Helicopter-borne observations with unprecedented high resolution provide new insights in the fine-scale structure of marine boundary layer clouds and aerosol stratification over the Eastern North Atlantic.
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 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.
During the Aerosol and Cloud Experiment in the Eastern North Atlantic
(ACE-ENA), a variety of in situ optical
sensors using shadow imaging, scattering and holography were deployed
by the Atmospheric Radiation Measurement (ARM) Aerial Facility to
determine cloud properties. Taking advantage of the wide, overlapping
range of instrumentation, we compare in
situ cloud data from several different measurement methods
for droplets up to 100 µm. Data processing was tailored to the
encountered conditions, leading to good agreement. Improvements
include noise reduction for holography and better out-of-focus
correction for shadow imaging. Comparison between direct liquid water
content measurements and optical sensors showed better agreement at
higher droplet number concentrations (>120/cm3).
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 machine-learning algorithm for identifying clusters of samples with similar properties: Density-based spatial clustering of applications (DBSCAN). 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 algorithm, 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 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.
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