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dc.contributor.authorvan Lier-Walqui, Marcus
dc.contributor.authorMorrison, Hugh
dc.contributor.authorKumjian, Matthew R.
dc.contributor.authorReimel, Karly J.
dc.contributor.authorPrat, Olivier P.
dc.contributor.authorLunderman, Spencer
dc.contributor.authorMorzfeld, Matthias
dc.date.accessioned2020-09-02T23:30:37Z
dc.date.available2020-09-02T23:30:37Z
dc.date.issued2020-03-04
dc.identifier.citationvan Lier-Walqui, M., H. Morrison, M. R. Kumjian, K. J. Reimel, O. P. Prat, S. Lunderman, and M. Morzfeld, 2020: A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments. J. Atmos. Sci., 77, 1043–1064, https://doi.org/10.1175/JAS-D-19-0071.1.en_US
dc.identifier.issn0022-4928
dc.identifier.doi10.1175/jas-d-19-0071.1
dc.identifier.urihttp://hdl.handle.net/10150/642369
dc.description.abstractObservationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical-Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme's development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS-flexibility being a key feature of this approach-are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit-a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates.en_US
dc.language.isoenen_US
dc.publisherAMER METEOROLOGICAL SOCen_US
dc.rightsCopyright © 2020 American Meteorological Society.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectAtmosphereen_US
dc.subjectCloud microphysicsen_US
dc.subjectRadarsen_US
dc.subjectRadar observationsen_US
dc.subjectBayesian methodsen_US
dc.subjectCloud parameterizationsen_US
dc.subjectModel errorsen_US
dc.titleA Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experimentsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Mathen_US
dc.identifier.journalJOURNAL OF THE ATMOSPHERIC SCIENCESen_US
dc.description.note6 month embargo; published online: 4 March 2020en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleJournal of the Atmospheric Sciences
dc.source.volume77
dc.source.issue3
dc.source.beginpage1043
dc.source.endpage1064
refterms.dateFOA2020-09-04T00:00:00Z


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