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DOI | 10.1175/JAS-D-19-0070.1 |
A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description | |
Morrison, Hugh1; van Lier-Walqui, Marcus2,3; Kumjian, Matthew R.4; Prat, Olivier P.5 | |
2020-03-01 | |
发表期刊 | JOURNAL OF THE ATMOSPHERIC SCIENCES |
ISSN | 0022-4928 |
EISSN | 1520-0469 |
出版年 | 2020 |
卷号 | 77期号:3页码:1019-1041 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | 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 (denoted as 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 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. |
英文关键词 | Atmosphere Cloud microphysics Radars Radar observations Bayesian methods Cloud parameterizations Model errors |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000526717000001 |
WOS关键词 | SQUALL LINE ; CLOUD MICROPHYSICS ; TERMINAL VELOCITY ; UNCERTAINTY ; EVAPORATION ; MODEL ; PREDICTION ; IMPACT ; PRECIPITATION ; SIMULATIONS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280290 |
专题 | 地球科学 |
作者单位 | 1.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA; 2.NASA, Goddard Inst Space Studies, New York, NY 10025 USA; 3.Columbia Univ, Ctr Climate Syst Res, New York, NY 10027 USA; 4.Penn State Univ, Dept Meteorol & Atmospher Sci, University Pk, PA 16802 USA; 5.North Carolina State Univ, North Carolina Inst Climate Studies, Asheville, NC USA |
推荐引用方式 GB/T 7714 | Morrison, Hugh,van Lier-Walqui, Marcus,Kumjian, Matthew R.,et al. A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description[J]. JOURNAL OF THE ATMOSPHERIC SCIENCES,2020,77(3):1019-1041. |
APA | Morrison, Hugh,van Lier-Walqui, Marcus,Kumjian, Matthew R.,&Prat, Olivier P..(2020).A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description.JOURNAL OF THE ATMOSPHERIC SCIENCES,77(3),1019-1041. |
MLA | Morrison, Hugh,et al."A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description".JOURNAL OF THE ATMOSPHERIC SCIENCES 77.3(2020):1019-1041. |
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