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.

中文翻译：

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应对云建模和降水微物理的挑战

在大气中，微观物理学是指影响云和降水粒子的微观过程，是地球大气水和能量循环各个组成部分之间的关​​键联系。模型中微物理过程的表示仍然是一个重大挑战，导致数值天气预报和气候模拟的不确定性。在本文中，模型中处理微物理的问题分为两部分：（i）考虑到不可能单独模拟云内的所有粒子，如何表示云和降水粒子的总体；（ii）云中的不确定性。由于云物理学知识的根本差距而导致的微物理过程速率。最近开发的基于拉格朗日粒子的方法被提倡作为解决使用传统体和箱微物理参数化方案表示粒子群的几个概念和实际挑战的方法。为了解决云物理知识的关键差距，需要对实验室实验、新探测器开发和下一代太空仪器的观测进展进行持续投资。人们认为，对实验室工作的重视程度在过去几十年中相对于云物理研究的其他领域明显有所下降，但被认为是提高过程层面理解的重要因素。还提倡更系统地使用自然云和降水观测来约束微物理方案。由于通常很难直接从这些观察中量化各个微物理过程速率，因此这提出了一个可以从贝叶斯统计的角度来看的逆问题。按照这个想法，提出了一个概率框架，该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之外，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，以加速微物理方案的改进，利用本文描述的与过程建模（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。