Publication Abstracts

Morrison et al. 2019, submitted

Morrison, H.C., M. van Lier-Walqui, M.R. Kumjian, and O.P. Prat, 2019: A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part I: Scheme description. J. Atmos. Sci., submitted.

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part 2 of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

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BibTeX Citation

@unpublished{mo04200y,
  author={Morrison, H. C. and van Lier-Walqui, M. and Kumjian, M. R. and Prat, O. P.},
  title={A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part I: Scheme description},
  year={2019},
  journal={J. Atmos. Sci.},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - mo04200y
AU  - Morrison, H. C.
AU  - van Lier-Walqui, M.
AU  - Kumjian, M. R.
AU  - Prat, O. P.
PY  - 2019
TI  - A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part I: Scheme description
JA  - J. Atmos. Sci.
ER  -

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