Title: A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

Abstract

A novel 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. Furthermore, 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 (denoted as MORR). It is demonstrated 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 ofmore » MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II 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.« less

Authors:

Morrison, Hugh ^[1]; van Lier-Walqui, Marcus ^[2]; Kumjian, Matthew R. ^[3]; Prat, Olivier P. ^[4]

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+ Show Author Affiliations

1. National Center for Atmospheric Research, Boulder, CO (United States)
2. NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Columbia Univ., New York, NY (United States)
3. Pennsylvania State Univ., University Park, PA (United States)
4. North Carolina State Univ., Raleigh, NC (United States)

Publication Date:

Wed Mar 04 00:00:00 EST 2020

Research Org.:

Columbia Univ., New York, NY (United States)

Sponsoring Org.:

Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division; National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:

1632342

Alternate Identifier(s):

OSTI ID: 1844479

Grant/Contract Number:  

SC0016579

Resource Type:

Accepted Manuscript

Journal Name:

Journal of the Atmospheric Sciences

Additional Journal Information:

Journal Volume: 77; Journal Issue: 3; Journal ID: ISSN 0022-4928

Publisher:

American Meteorological Society

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES; Atmosphere; Cloud microphysics; Radars/Radar observations; Bayesian methods; Cloud parameterizations; Model errors