Publication Abstracts

Van Lier-Walqui et al. 2019, submitted

Van Lier-Walqui, M., H.C. Morrison, M.R. Kumjian, K.J. Karly J. Reimel, O.P. Prat, S. Lunderman, and M. Morzfeld, 2019: A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part II: Idealized Markov chain Monte Carlo experiments. J. Atmos. Sci., submitted.

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 rain shaft 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 3rd 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 rain shaft 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.

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BibTeX Citation

@unpublished{va08300m,
  author={van Lier-Walqui, M. and Morrison, H. C. and Kumjian, M. R. and Karly J. Reimel, K. J. and Prat, O. P. and Lunderman, S. and Morzfeld, M.},
  title={A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part II: Idealized Markov chain Monte Carlo experiments},
  year={2019},
  journal={J. Atmos. Sci.},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - va08300m
AU  - van Lier-Walqui, M.
AU  - Morrison, H. C.
AU  - Kumjian, M. R.
AU  - Karly J. Reimel, K. J.
AU  - Prat, O. P.
AU  - Lunderman, S.
AU  - Morzfeld, M.
PY  - 2019
TI  - A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part II: Idealized Markov chain Monte Carlo experiments
JA  - J. Atmos. Sci.
ER  -

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