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

2006 ◽  
Vol 45 (9) ◽  
pp. 1282-1290 ◽  
Author(s):  
Brad Baker ◽  
R. Paul Lawson

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 = αLβ. 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.

2006 ◽  
Vol 45 (9) ◽  
pp. 1291-1303 ◽  
Author(s):  
R. Paul Lawson ◽  
Brad A. Baker

Abstract In Part I of this two-part series, a new relationship for ice particle mass M was derived based on an expanded dataset of photographed ice particles and melted drops. The new relationship resulted in a reduction of nearly 50% in the rms error in M. In this paper, new relationships for computing particle mass and ice water content from 2D particle imagery are compared with other relationships previously used in the literature. Comparison of the old and new relationships, when applied to data collected in natural clouds, shows that results using the old relationships differ from the new relationships by up to a factor of 3, depending on particle size and shape. One of the new relationships can be applied to existing (archived) datasets of two-dimensional images, provided that the number of occulted pixels in each image (i.e., projected area) is available.


2008 ◽  
Vol 113 (D5) ◽  
pp. n/a-n/a ◽  
Author(s):  
D. S. Sayres ◽  
J. B. Smith ◽  
J. V. Pittman ◽  
E. M. Weinstock ◽  
J. G. Anderson ◽  
...  

2021 ◽  
Vol 254 ◽  
pp. 112242
Author(s):  
Eugenio Gorgucci ◽  
Luca Baldini ◽  
Elisa Adirosi ◽  
Mario Montopoli

2016 ◽  
Vol 16 (16) ◽  
pp. 10609-10620 ◽  
Author(s):  
Johannes Bühl ◽  
Patric Seifert ◽  
Alexander Myagkov ◽  
Albert Ansmann

Abstract. An analysis of the Cloudnet data set collected at Leipzig, Germany, with special focus on mixed-phase layered clouds is presented. We derive liquid- and ice-water content together with vertical motions of ice particles falling through cloud base. The ice mass flux is calculated by combining measurements of ice-water content and particle Doppler velocity. The efficiency of heterogeneous ice formation and its impact on cloud lifetime is estimated for different cloud-top temperatures by relating the ice mass flux and the liquid-water content at cloud top. Cloud radar measurements of polarization and Doppler velocity indicate that ice crystals formed in mixed-phase cloud layers with a geometrical thickness of less than 350 m are mostly pristine when they fall out of the cloud.


2021 ◽  
Author(s):  
Lyle E. Lilie ◽  
Dan Bouley ◽  
Christopher P. Sivo ◽  
John W. Strapp ◽  
Thomas P. Ratvasky

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