Title: Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract

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. Itmore » 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 Statement A 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.« less

Authors:

Allwayin, Nithin  ^[1];

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Larsen, Michael L. ^[2]; Shaw, Alexander G. ^[3]; Shaw, Raymond A. ^[1]

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1. a Michigan Technological University, Houghton, Michigan
2. a Michigan Technological University, Houghton, Michigan, b College of Charleston, Charleston, South Carolina
3. c Brigham Young University, Provo, Utah

Publication Date:

Fri Jul 01 00:00:00 EDT 2022

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

OSTI Identifier:

1887478

Alternate Identifier(s):

OSTI ID: 1884127

Grant/Contract Number:  

SC0020053; AGS-2001490

Resource Type:

Published Article

Journal Name:

Artificial Intelligence for the Earth Systems

Additional Journal Information:

Journal Name: Artificial Intelligence for the Earth Systems Journal Volume: 1 Journal Issue: 3; Journal ID: ISSN 2769-7525

Publisher:

American Meteorological Society

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; Cloud droplets; Cloud microphysics; In situ atmospheric observations; Machine learning; Microscale processes/variability