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?

等效的气候敏感性（即二氧化碳加倍的变暖）是评估二氧化碳排放引起的风险的基本指标。然而，气候敏感性的合理值在40年中一直固执地不确定，云反馈是特别不确定的组成部分（Sherwood等，2020）。深层转化产生的热带高云（例如砧）是反馈时的重要云类型。 IPCC评估报告6最近评估了热带高云量的负反馈（Forster等，2021）。然而，这是由于对微物理学和湍流对变暖的反应缺乏理解而产生的部分是由于缺乏理解。云冰微物理学的参数尤其差。 2022年7月至8月，DCMEX运动成功地收集了一系列在新墨西哥州马格达莱纳山脉上发展对流云的大量观察。 FAAM BAE-146飞机在云中测量了云的微物理学和动力学，而多普勒雷达和自动相机则监测了附近的云的发展。在山脉山顶上的飞机和Langmuir实验室收集了包括冰核颗粒（INP）在内的气溶胶测量值。现在，可以将此广泛的数据集与卫星数据结合使用，并与最近开发的MET Office Unified Model Casim Microphysics方案进行建模。总体而言，数据将通过改善全球气候模型中的微物理过程的表示来支持气候敏感性不确定性。已经获得了云冰颗粒的测定观察结果，其中包含大量信息的复杂性。气溶胶的特征，冰颗粒的大小和形状及其在云中的位置，以不同的方式改变了微物理过程和云辐射效应（Voigtländer等，2018; Gasparini et al。，2019; Diest; Doodeenenhoven et al。，2020;气溶胶和冰过程的复杂性以及云响应是使用新颖的分析技术（例如机器学习）来获得洞察力的合理性。博士候选人将通过探索一系列分析技术来基于正在进行的DCMEX研究。最初的重点将放在可以从监督和无监督的机器学习技术中学到的知识，这些技术在研究云和气候研究中迅速成为关键工具（Gagne等，2017； Beucler等，2021; Kashinath等，2021; 2021;，Gettelman等，2021）。最初，这项工作将集中于了解冰颗粒形成过程，以支持UM-CASIM模型的开发。然后，该研究将扩展，以将动态微物理过程与对辐射的影响以及风暴的电气化联系起来。通过扩展分析以考虑电气化，我们建立了对全球可变性，闪电的理解，这是感兴趣的深对流风暴的关键特征。这是一个机会，不要错过DCMEX研究区域中运行的详细卫星（GON GOL）和基于地面的雷电检测网络。以下研究问题将被解决：1）可以对云中的冰图像进行分类并与它们的形成动态有关吗？ INP在此中扮演着什么作用？2）机器学习能够使用观察值来约束UM-Casim模型参数和过程？3）3）如何受到云动力学和微物理学影响深度对流的Anvil的辐射如何？4）闪电活动和云的磁态和动力学之间的关系是什么？