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dc.contributor.authorHsieh, W.C.
dc.contributor.authorJonsson, H.
dc.contributor.authorWang, L.-P.
dc.contributor.authorFlagan, R.C.
dc.contributor.authorSeinfeld, J.H.
dc.contributor.authorNenes, A.
dc.contributor.authorBuzorius, G.
dc.date2009
dc.date.accessioned2014-06-11T23:18:37Z
dc.date.available2014-06-11T23:18:37Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10945/42257
dc.descriptionJournal of Geophysical Research, Vol. 114, D07201en_US
dc.descriptionThe article of record as published may be located at http://dx.doi.org/10.1029/en_US
dc.description.abstractIn this study, we evaluate eight autoconversion parameterizations against integration of the Kinetic Collection Equation (KCE) for cloud size distributions measured during the NASA CRYSTAL-FACE and CSTRIPE campaigns. KCE calculations are done using both the observed data and fits of these data to a gamma distribution function; it is found that the fitted distributions provide a good approximation for calculations of total coalescence but not for autoconversion because of fitting errors near the drop-drizzle separation size. Parameterizations that explicitly compute autoconversion tend to be in better agreement with KCE but are subject to substantial uncertainty, about an order of magnitude in autoconversion rate. Including turbulence effects on droplet collection increases autoconversion by a factor of 1.82 and 1.24 for CRYSTAL-FACE and CSTRIPE clouds, respectively; this enhancement never exceeds a factor of 3, even under the most aggressive collection conditions. Shifting the droplet-drizzle separation radius from 20 to 25 mm results in about a twofold uncertainty in autoconversion rate. The polynomial approximation to the gravitation collection kernel used to develop parameterizations provides computation of autoconversion that agree to within 30%. Collectively, these uncertainties have an important impact on autoconversion but are all within the factor of 10 uncertainty of autoconversion parameterizations. Incorporating KCE calculations in GCM simulations of aerosol-cloud interactions studies is computationally feasible by using precalculated collection kernel tables and can quantify the autoconversion uncertainty associated with application of parameterizations.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleOn the representation of droplet coalescence and autoconversion: Evaluation using ambient cloud droplet size distributionsen_US
dc.contributor.departmentCenter for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS)
dc.contributor.departmentMeteorology


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