Exploiting machine-learning to provide dynamical, microphysical, radiative and electrifying insight from observations of deep convective cloud

利用机器学习从深对流云的观测中提供动态、微观物理、辐射和令人兴奋的见解

• 批准号: 2888807
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888807
• 关键词: Exploiting; machine; learning; provide; dynamical

The equilibrium climate sensitivity (i.e. the warming from a doubling of CO2) is a fundamental metric for assessing the risks arising from CO2 emissions. Yet the plausible values of climate sensitivity have remained stubbornly uncertain for 40 years, with cloud feedbacks a particularly uncertain component (Sherwood et al., 2020). Tropical high cloud (e.g. anvils), produced by deep convection, is an important cloud type when it comes to feedbacks. The IPCC Assessment Report 6 recently assessed there to be a negative feedback from tropical high cloud amount (Forster et al., 2021). This, however, came with low confidence that arises, in part, from the lack of understanding of the response of microphysics and turbulence to warming. Cloud ice microphysics is particularly poorly parametrised. In July-August 2022, the DCMEX campaign successfully collected a vast set of observations of developing convective clouds over the Magdalena Mountains, New Mexico. The FAAM BAe-146 aircraft measured cloud microphysics and dynamics within the clouds whilst Doppler radars and automated cameras monitored the development of the clouds from nearby. Aerosol measurements, including of Ice Nucleating Particles (INP), were collected on the aircraft and at Langmuir Laboratory on the summit of the mountain range. This extensive dataset can now be analysed in combination with satellite data and modelling with the recently developed Met Office Unified Model CASIM microphysics scheme. Altogether, the data will support the reduction of climate sensitivity uncertainty by improving the representation of microphysical processes in global climate models.Detailed observations of aerosol and imagery of cloud ice particles have been obtained which contain a great complexity of information. The characteristics of aerosol, and the size and shape of ice particles, as well as their position within the cloud, modify the microphysical processes and cloud radiative effect in different ways (Voigtländer et al., 2018; Gasparini et al., 2019; Diedenhoven et al, 2020). The complexity of the aerosol and ice processes, and the cloud response, justifies using novel analysis techniques, such as machine learning, to gain insight. The PhD candidate will build upon ongoing DCMEX research by exploring a range of analysis techniques. An initial focus will be on what can be learned from both supervised and unsupervised machine learning techniques, which are fast becoming key tools in the study of clouds and climate (Gagne et al., 2017; Beucler et al., 2021; Kashinath et al, 2021;,Gettelman et al., 2021). Initially, the work will focus on understanding the ice particle formation processes to support the development of the UM-CASIM model. The research will then expand to relate the dynamical-microphysical processes to impacts on radiation, and also electrification of storms. By extending the analysis to consider electrification, we build understanding of a globally measured variable, lightning, which is a key feature of the deep convective storms of interest. This is an opportunity not to be missed given the detailed satellite (GOES GLM) and ground-based lightning detection networks in operation over the DCMEX study region. The following research questions will be addressed:1) Can in-cloud ice images be categorised and related to their formation dynamics? And what role do INPs play in this?2) Is machine-learning able to constrain UM-CASIM model parameters and processes using observations?3) How is the radiation at the deep convective anvil affected by cloud dynamics and microphysics?4) What is the relationship between lightning activity and cloud microphysics and dynamics?5) Can lightning be used as an indicator of cloud processes that result in variations of anvil radiative properties?

平衡气候敏感度(即二氧化碳增加一倍所致的升温)是评估二氧化碳排放风险的基本指标。然而，40年来，气候敏感性的似是而非的值一直顽固地保持着不确定性，云反馈是一个特别不确定的组成部分(Sherwood等人，2020年)。热带高压云是一种重要的反馈云型，它是由深对流产生的。气专委评估报告6最近评估了热带高云量的负面反馈(Forster等人，2021年)。然而，这在一定程度上是因为缺乏对微物理和湍流对气候变暖的反应的理解，因此信心不足。云冰微物理的参数化特别差。2022年7月至8月，DCMEX活动成功地收集了新墨西哥州马格达莱纳山脉上空发展对流云的大量观测数据。FAAM BAE-146飞机测量了云层的微物理和云层内的动力学，而多普勒雷达和自动相机则从附近监测云层的发展。在飞机上和位于山脉顶端的朗缪尔实验室收集了气溶胶测量数据，包括冰核粒子(INP)。现在可以结合卫星数据和最近开发的气象局统一模型CASIM微物理方案对这一广泛的数据集进行分析。总之，这些数据将通过改进全球气候模式中微物理过程的表示来支持减少气候敏感性的不确定性。气溶胶的特性、冰粒的大小和形状以及它们在云中的位置以不同的方式改变微物理过程和云辐射效应(Voigtländer等人，2018年；Gasparini等人，2019；Diedenhoven等人，2020年)。气溶胶和冰层过程的复杂性，以及云的响应，证明有理由使用新的分析技术，如机器学习，以获得洞察。博士生将通过探索一系列分析技术，在正在进行的DCMEX研究的基础上再接再厉。最初的重点将是从监督和非监督机器学习技术中学到什么，这些技术正迅速成为云和气候研究的关键工具(Gagne等人，2017；Beucler等人，2021；Kashinath等人，2021；Gettelman等人，2021)。最初，这项工作将侧重于了解冰粒形成过程，以支持UM-CASIM模式的发展。然后，这项研究将扩大到将动力-微物理过程与对辐射的影响以及风暴的带电联系起来。通过将分析扩展到考虑带电，我们建立了对全球测量变量闪电的理解，闪电是所关注的深对流风暴的关键特征。这是一个不容错过的机会，因为DCMEX研究区上空运行着详细的卫星(GOES GLM)和地面闪电探测网络。将解决以下研究问题：1)云中冰图像是否可以分类并与其形成动力学相关？2)机器学习是否能够通过观测来约束UM-CASIM模式的参数和过程？3)云动力学和微物理是如何影响深对流砧处的辐射的？4)闪电活动与云微物理和动力学之间的关系是什么？5)闪电能否被用作云过程的指示器，从而导致砧座辐射特性的变化？