A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments
Author
van Lier-Walqui, MarcusMorrison, Hugh

Kumjian, Matthew R.
Reimel, Karly J.
Prat, Olivier P.
Lunderman, Spencer
Morzfeld, Matthias
Affiliation
Univ Arizona, Dept MathIssue Date
2020-03-04Keywords
AtmosphereCloud microphysics
Radars
Radar observations
Bayesian methods
Cloud parameterizations
Model errors
Metadata
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AMER METEOROLOGICAL SOCCitation
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.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 2020ISSN
0022-4928Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1175/jas-d-19-0071.1