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DOI | 10.1175/JAS-D-19-0071.1 |
A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments | |
Van Lier-Walqui, Marcus1,2; Morrison, Hugh3; Kumjian, Matthew R.4; Reimel, Karly J.4; Prat, Olivier P.5; Lunderman, Spencer6; Morzfeld, Matthias6 | |
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:1043-1064 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | 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. |
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:000526717000002 |
WOS关键词 | POLARIMETRIC RADAR ; IMPACT ; UNCERTAINTY |
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/280291 |
专题 | 地球科学 |
作者单位 | 1.Columbia Univ, Ctr Climate Syst Res, New York, NY 10027 USA; 2.NASA, Goddard Inst Space Studies, New York, NY 10025 USA; 3.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 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; 6.Univ Arizona, Dept Math, Tucson, AZ 85721 USA |
推荐引用方式 GB/T 7714 | Van Lier-Walqui, Marcus,Morrison, Hugh,Kumjian, Matthew R.,et al. A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments[J]. JOURNAL OF THE ATMOSPHERIC SCIENCES,2020,77(3):1043-1064. |
APA | Van Lier-Walqui, Marcus.,Morrison, Hugh.,Kumjian, Matthew R..,Reimel, Karly J..,Prat, Olivier P..,...&Morzfeld, Matthias.(2020).A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments.JOURNAL OF THE ATMOSPHERIC SCIENCES,77(3),1043-1064. |
MLA | Van Lier-Walqui, Marcus,et al."A Bayesian Approach for Statistical-Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments".JOURNAL OF THE ATMOSPHERIC SCIENCES 77.3(2020):1043-1064. |
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