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?

平衡气候敏感性（即二氧化碳加倍导致的变暖）是评估二氧化碳排放风险的基本指标，但气候敏感性的合理值在 40 年来一直保持不确定性，尤其是云反馈的不确定性。深对流产生的热带高空云（例如砧云）是 IPCC 评估报告 6 的重要云类型。最近评估认为热带高云量存在负反馈（Forster 等人，2021），但这种情况的可信度较低，部分原因是缺乏对微物理和湍流对变暖的响应的了解。云冰微物理的参数化尤其糟糕。2022 年 7 月至 8 月，DCMEX 活动成功收集了新州马格达莱纳山脉上空发展对流云的大量观测数据。墨西哥 FAAM BAe-146 飞机测量了云层内部的微物理和动力学，同时多普勒雷达和自动摄像机监测了附近云层的发展，包括在飞机上和在空中收集的气溶胶测量数据。位于山顶的朗缪尔实验室现在可以结合卫星数据和最近开发的英国气象局统一模型 CASIM 微物理方案进行分析。总而言之，这些数据将通过改善全球气候模型中微物理过程的表示来支持减少气候敏感性的不确定性。已经获得了气溶胶和云冰颗粒图像的详细观测，其中包含非常复杂的信息。冰颗粒的大小和形状，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，2018；Gasparini 等， 2019 年；Diedenhoven 等人，2020 年）。气溶胶和冰过程以及云响应的复杂性证明了使用机器学习等新颖的分析技术来获取见解的合理性。最初的重点将是从有监督和无监督的机器学习技术中学到什么，这些技术正在迅速成为云和气候研究的关键工具（Gagne 等人， 2017 年；Beucler 等人，2021 年；Kashinath 等人，2021 年；Gettelman 等人，2021 年）。然后将扩展以将动态微物理过程与辐射的影响以及风暴的带电联系起来。通过扩展分析以考虑带电，我们建立了对全球的理解。测量变量闪电，这是感兴趣的深对流风暴的一个关键特征，考虑到接下来在 DCMEX 研究区域运行的详细卫星 (GOES GLM) 和地面闪电探测网络，这是一个不容错过的机会。将解决的研究问题：1）云内冰图像可以分类并与其形成动力学相关吗？INP 在其中发挥什么作用？2）机器学习是否能够使用约束 UM-CASIM 模型参数和过程？观测结果？3) 深对流砧处的辐射如何受到云动力学和微物理的影响？4) 闪电活动与云微物理和动力学之间的关系是什么？5) 闪电能否用作导致云过程的指标？砧座辐射特性的变化？