A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description
A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description
复制标题
雨微物理统计物理体参数化的贝叶斯方法第一部分：方案描述。
DOI：
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发表时间：
2019
影响因子：
3.1
通讯作者：
O. Prat
中科院分区：
文献类型：
--
作者：
H. Morrison;M. Lier;M. Kumjian;O. Prat
为雨微物理的整体参数化提出了一个新框架：贝叶斯观测约束统计物理方案（BOSS）。它旨在促进使用贝叶斯推理进行观察的直接约束。 BOSS 将现有的过程级微观物理知识与灵活的过程速率公式和受贝叶斯框架内的观察约束的参数相结合。使用通过广义幂级数将 DSD 矩相互关联的雨滴尺寸分布 (DSD) 归一化方法，导出微物理过程速率的广义多元幂表达式作为一组预测 DSD 矩的函数。该方案很灵活，可以利用任意数量的预测时刻及其组合以及过程速率公式中的任意数量的项。这意味着可以系统地研究参数值的不确定性和与过程速率公式相关的结构不确定性，这是使用传统方案不可能实现的。在本文中，BOSS 与传统整体降雨微物理方案（表示为 MORR）的两矩和三矩版本进行了比较。结果表明，MORR 中的某些过程公式在分析上与 BOSS 中使用一项或两项的广义幂表达式是等价的，而其他过程公式则不然。 BOSS 能够在理想化的一维雨井测试中复制 MORR 的行为，但设计更加灵活和系统。本研究的第二部分描述了应用 BOSS 在贝叶斯实验中使用受合成观测约束的马尔可夫链蒙特卡罗采样来推导降雨微物理过程速率和后验参数分布。
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.
影响因子：
5.2
作者：
Gentine, P.;Pritchard, M.;Yacalis, G.
通讯作者：
Yacalis, G.
DOI：
10.1073/pnas.1810286115
发表时间：
2018-09-25
影响因子：
11.1
作者：
Rasp S;Pritchard MS;Gentine P
通讯作者：
Gentine P