Morrison Hugh, van Lier-Walqui Marcus, Fridlind Ann M, Grabowski Wojciech W, Harrington Jerry Y, Hoose Corinna, Korolev Alexei, Kumjian Matthew R, Milbrandt Jason A, Pawlowska Hanna, Posselt Derek J, Prat Olivier P, Reimel Karly J, Shima Shin-Ichiro, van Diedenhoven Bastiaan, Xue Lulin
National Center for Atmospheric Research Boulder CO USA.
NASA Goddard Institute for Space Studies and Center for Climate Systems Research Columbia University New York NY USA.
J Adv Model Earth Syst. 2020 Aug;12(8):e2019MS001689. doi: 10.1029/2019MS001689. Epub 2020 Jul 31.
In the atmosphere, 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.
在大气中，[具体所指未给出]是指影响云与降水粒子的微尺度过程，并且是地球大气水和能量循环各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存在不确定性。在本文中，模型中处理微物理的问题分为两部分：（i）鉴于在云中单独模拟所有粒子是不可能的，如何表示云与降水粒子的总体；（ii）由于云物理学知识的根本差距导致的微物理过程速率的不确定性。最近开发的基于拉格朗日粒子的方法被倡导为一种解决使用传统的体参数化和分档微物理参数化方案来表示粒子总体所面临的几个概念和实际挑战的途径。为了解决云物理学知识中的关键差距，需要持续投入以推动来自实验室实验、新探测器开发以及下一代空间仪器的观测进展。有人认为，相对于云物理学研究的其他领域，过去几十年实验室工作明显减少，而更加强调实验室工作是增进对过程层面理解的关键要素。还倡导更系统地利用自然云与降水观测来约束微物理方案。由于通常很难直接从这些观测中量化单个微物理过程速率，这就提出了一个反问题，可以从贝叶斯统计的角度来看待。遵循这一思路，提出了一个概率框架，它结合了统计建模和物理建模的要素。除了对方案提供严格约束外，还有系统量化不确定性的额外好处。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、云与降水观测以及统计方法相关的进展。
