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Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I: Image-to-Mass Relationships

Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I:... Ice water content in natural clouds is an important but difficult quantity to measure. The goal of a number of past studies was to find average relationships between the masses and lengths of ice particles to determine ice water content from in situ data, such as those routinely recorded with two-dimensional imaging probes. The general approach in these past studies was to measure maximum length L and mass M of a dataset of ice crystals collected at a ground site. Linear regression analysis was performed on the logarithms of the data to estimate an average mass-to-length relationship of the form M = αL β . Relationships were determined for subsets of the dataset based on crystal habit (shape) as well as for the full dataset. In this study, alternative relationships for determining mass using the additional parameters of width W , area A , and perimeter P are explored. A 50% reduction in rms error in the determination of mass relative to using L alone is achieved using a single parameter that is a combination of L , W , A , and P . The new parameter is designed to take into account the shape of the ice particle without the need to classify the crystals first. An interesting result is that, when applied to the test dataset, the same reduction in rms error is also shown to be achievable using A alone. Using A alone facilitates the reanalysis and improvement of the determination of ice water content from large existing datasets of two-dimensional images, because A is simply the number of occulted pixels in the digital images. Possible sources of error in this study are investigated, as is the usefulness of first segregating the particles into crystal habits. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I: Image-to-Mass Relationships

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Publisher
American Meteorological Society
Copyright
Copyright © 2004 American Meteorological Society
ISSN
1558-8424
eISSN
1558-8432
DOI
10.1175/JAM2398.1
Publisher site
See Article on Publisher Site

Abstract

Ice water content in natural clouds is an important but difficult quantity to measure. The goal of a number of past studies was to find average relationships between the masses and lengths of ice particles to determine ice water content from in situ data, such as those routinely recorded with two-dimensional imaging probes. The general approach in these past studies was to measure maximum length L and mass M of a dataset of ice crystals collected at a ground site. Linear regression analysis was performed on the logarithms of the data to estimate an average mass-to-length relationship of the form M = αL β . Relationships were determined for subsets of the dataset based on crystal habit (shape) as well as for the full dataset. In this study, alternative relationships for determining mass using the additional parameters of width W , area A , and perimeter P are explored. A 50% reduction in rms error in the determination of mass relative to using L alone is achieved using a single parameter that is a combination of L , W , A , and P . The new parameter is designed to take into account the shape of the ice particle without the need to classify the crystals first. An interesting result is that, when applied to the test dataset, the same reduction in rms error is also shown to be achievable using A alone. Using A alone facilitates the reanalysis and improvement of the determination of ice water content from large existing datasets of two-dimensional images, because A is simply the number of occulted pixels in the digital images. Possible sources of error in this study are investigated, as is the usefulness of first segregating the particles into crystal habits.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Dec 29, 2004

References