Confronting the Challenge of Modeling Cloud and Precipitation Microphysics.
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics.
复制标题
面对云和降水微物理建模的挑战。
DOI：
10.1029/2019ms001689
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发表时间：
2020-08
影响因子：
6.8
通讯作者：
Xue L
中科院分区：
文献类型：
--
作者：
Morrison H;van Lier-Walqui M;Fridlind AM;Grabowski WW;Harrington JY;Hoose C;Korolev A;Kumjian MR;Milbrandt JA;Pawlowska H;Posselt DJ;Prat OP;Reimel KJ;Shima SI;van Diedenhoven B;Xue L
在大气中，微物理是指影响云和降水粒子的微尺度过程，是地球大气水和能量循环各组成部分之间的关键纽带。模式中微物理过程的表示继续构成一个重大挑战，导致数值天气预报和气候模拟的不确定性。在本文中，在模型中处理微物理问题分为两个部分：(i)如何表示云和降水粒子的总体，因为不可能在云中单独模拟所有粒子；（ii）由于云物理知识的基本差距，微物理过程速率存在不确定性。最近发展的基于拉格朗日粒子的方法被认为是一种解决使用传统的体和箱微物理参数化方案来表示粒子群的几个概念和实践挑战的方法。为了解决云物理知识方面的关键空白，需要持续投资于实验室实验、新探测器开发和下一代空间仪器的观测进展。在过去的几十年里，相对于云物理研究的其他领域，实验室工作的重视程度明显下降，这被认为是提高过程级理解的重要因素。还提倡更系统地利用自然云和降水观测来约束微物理方案。因为通常很难直接从这些观察中量化个体微物理过程速率，这就提出了一个可以从贝叶斯统计的角度来看的反问题。根据这个想法，提出了一个概率框架，结合了统计和物理建模的元素。除了提供严格的方案约束外，系统地量化不确定性还有一个额外的好处。最后，提出了一种更广泛的分层方法来加速微物理方案的改进，利用本文中描述的与过程建模（使用基于拉格朗日粒子的方案）、实验室实验、云和降水观测以及统计方法相关的进展。微物理是天气和气候模式的重要组成部分，但其在当前模式中的表示存在高度不确定性，确定了两个关键挑战：表示云和降水粒子群以及云物理中的知识差距
In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes