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Parameterization of cloud droplet size distributions: Comparison with parcel models and observations
Author(s) -
Hsieh W. C.,
Nenes A.,
Flagan R. C.,
Seinfeld J. H.,
Buzorius G.,
Jonsson H.
Publication year - 2009
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2008jd011387
Subject(s) - adiabatic process , cloud computing , dispersion (optics) , atmospheric dispersion modeling , effective radius , meteorology , liquid water content , statistical physics , mechanics , cloud height , radius , environmental science , atmospheric sciences , physics , cloud cover , thermodynamics , optics , computer science , operating system , air pollution , chemistry , computer security , organic chemistry , quantum mechanics , galaxy
This work examines the efficacy of various physically based approaches derived from one‐dimensional adiabatic parcel model frameworks (a numerical model and a simplified parameterization) to parameterize the cloud droplet distribution characteristics for computing cloud effective radius and autoconversion rate in regional/global atmospheric models. Evaluations are carried out for integrations with single (average) and distributions of updraft velocity, assuming that (1) conditions at s max are reflective of the cloud column or (2) cloud properties vary vertically, in agreement with one‐dimensional parcel theory. The predicted droplet distributions are then compared against in situ cloud droplet observations obtained during the CRYSTAL‐FACE and CSTRIPE missions. Good agreement of droplet relative dispersion between parcel model frameworks indicates that the parameterized parcel model essentially captures one‐dimensional dynamics; the predicted distributions are overly narrow, with relative dispersion being a factor of 2 lower than observations. However, if conditions at cloud maximum supersaturation are used to predict relative dispersion and applied throughout the cloud column, better agreement is seen with observations, especially if integrations are carried out over the distribution of updraft velocity. When considering the efficiency of the method, calculating cloud droplet spectral dispersion at s max is preferred for linking aerosol with droplet distributions in large‐scale models.

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