A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments
A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments
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雨微物理统计物理体参数化的贝叶斯方法第二部分：理想化马尔可夫链蒙特卡罗实验。
DOI：
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发表时间：
2019
影响因子：
3.1
通讯作者：
M. Morzfeld
中科院分区：
文献类型：
--
作者：
M. Lier;H. Morrison;M. Kumjian;K. Reimel;O. Prat;S. Lunderman;M. Morzfeld
论证了基于观测的大体积降雨微物理新框架的开发，即贝叶斯观测约束统计物理方案（BOSS；在本研究的第一部分中描述）。该方案的开发是由于与现有微物理方案中的近似和假设相关的云和天气模拟中存在巨大的不确定性。在这里，提出了一项概念验证研究，使用马尔可夫链蒙特卡罗采样算法和 BOSS 直接从一组综合生成的降雨观测中概率估计微物理过程速率和参数。所使用的框架是理想化的稳态一维柱雨轴模型，具有指定的柱顶降雨特性和固定的热力学剖面。 BOSS 的不同配置（灵活性是该方法的一个关键特征）受到传统三矩体微物理方案生成的综合观测的限制。说明了当真实参数值已知时检索正确参数值的能力。对于没有一组真实参数值的情况，比较不同复杂程度的BOSS配置的准确性。研究发现，添加第六时刻作为预报变量可以改善第三时刻（与降雨量成正比）和降雨率的预测。相比之下，通过添加更多幂项来增加过程速率公式的复杂性几乎没有什么好处——这一结果可以通过进一步理想化的实验来解释。 BOSS 雨井模拟被证明可以根据大量降雨观测的约束很好地估计真实过程速率，并具有这些估计的严格量化不确定性的额外好处。
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.
DOI：
10.1080/16000870.2017.1283809
发表时间：
2017-01-01
影响因子：
2
作者：
Morzfeld, Matthias;Hodyss, Daniel;Snyder, Chris
通讯作者：
Snyder, Chris