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    A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments

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    Author
    van Lier-Walqui, Marcus
    Morrison, Hugh cc
    Kumjian, Matthew R.
    Reimel, Karly J.
    Prat, Olivier P.
    Lunderman, Spencer
    Morzfeld, Matthias
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2020-03-04
    Keywords
    Atmosphere
    Cloud microphysics
    Radars
    Radar observations
    Bayesian methods
    Cloud parameterizations
    Model errors
    
    Metadata
    Show full item record
    Publisher
    AMER METEOROLOGICAL SOC
    Citation
    van 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.
    Journal
    JOURNAL OF THE ATMOSPHERIC SCIENCES
    Rights
    Copyright © 2020 American Meteorological Society.
    Collection Information
    This 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.
    Abstract
    Observationally 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.
    Note
    6 month embargo; published online: 4 March 2020
    ISSN
    0022-4928
    DOI
    10.1175/jas-d-19-0071.1
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1175/jas-d-19-0071.1
    Scopus Count
    Collections
    UA Faculty Publications

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