HUGS: a Hub for Uk Greenhouse gas data Science

HUGS：英国温室气体数据科学中心

• 批准号: NE/S016155/1
• 负责人: Matthew Rigby
• 金额: $ 27.33万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS016155%2F1
• 关键词: HUGS; Hub; Uk; Greenhouse; gas

Atmospheric observations of greenhouse gas (GHG) concentrations can be used to estimate emissions when combined with models of atmospheric transport and an understanding of the emission sources surrounding the observations. These top-down methods are complementary to the bottom-up, accounting-based, approaches that are currently used to create national GHG inventories. To improve the transparency and accuracy of these inventories and better evaluate progress on emissions reduction policies, scientists and policy makers have been advocating for the integration of top-down methods into the emissions reporting process. The United Nations Framework Convention on Climate Change (UNFCCC) recently acknowledged the important role that emissions quantified through atmospheric observations could have in supporting inventory evaluation (UNFCCC, COP 23, SBSTA/2017/L.21). The UK GHG science community is leading the world in this regard, with a dedicated national monitoring network, a range of regional networks and regular over-passes by various satellites. Currently, the UK is one of only three countries on Earth to include top-down estimates in its National Inventory Report to the UNFCCC. The process of inferring emissions from GHG observations is extremely data intensive. In order to understand the observed variability in GHG concentrations, scientists must combine data from diverse networks in different environments and using different instrumentation, understand the distribution of potential sources and land use types in the vicinity of the sensor and be able to accurately model the atmospheric processes that transport GHGs from sources to the measurement site. Therefore, to date, analysis of GHG data is largely carried out on a case-by-case basis for individual research papers.Here, we propose that new developments in cloud computing are required to help GHG scientists overcome some of the major obstacles for the integration of GHG networks and the production of operational, higher resolution GHG flux estimates. We will create the cloud-based framework for a UK GHG data science "hub". This hub will allow users (GHG scientists and, eventually, the public) to:- Improve the flow of information to and from GHG data providers, because cloud services are not behind institutional firewalls - Operationalise the processing of datasets into common formats, which can then be made globally accessible to users (subject to any required usage restrictions) - Automatically trigger operations on new data, such as the running of chemical transport models, which are essential for the interpretation of GHG data - Analyse data, model output and ancillary information (maps of land use, emissions inventories, etc.) on the cloud, without the need for individual users to download datasets and run models (requiring technical expertise)- Visualise data, models and other relevant information on a web-based platformOur team is world leading in the measurement and analysis of GHGs, cloud computing and spatial mapping. This project will rely heavily on a cloud platform (built as part of the EPSRC-funded BioSimSpace project) and GHG analysis codebase that has already been developed by team members. These tools are built on top of standard tools such as Jupyter notebooks, distributed object stores, and serverless functions. It is this expertise and these open tools that will allow us to develop the framework for our data science hub that will be extensible by GHG researchers at the end of this project.We envisage that such a hub could be at the centre of the UK's large and growing GHG science community, allowing scientists to upload, analyse and visualise their data on a single platform, enhancing data integration and sharing between groups. Ultimately, this platform could be extended to allow the public to interact with GHG data, letting them learn whether the UK's emissions reductions efforts are reflected in atmospheric observations.

温室气体浓度的大气观测，如果结合大气迁移模型和对观测周围排放源的了解，可用于估算排放量。这些自上而下的方法是对目前用于编制国家温室气体清单的自下而上、基于核算的方法的补充。为了提高这些清单的透明度和准确性，更好地评估减排政策的进展，科学家和决策者一直在倡导将自上而下的方法纳入排放报告过程。《联合国气候变化框架公约》（UNFCCC）最近承认了通过大气观测量化的排放在支持清单评估方面的重要作用（UNFCCC，COP 23，SBSTA/2017/L.21）。英国温室气体科学界在这方面处于世界领先地位，拥有专门的国家监测网络，一系列区域网络和各种卫星的定期飞越。目前，英国是地球上仅有的三个在其提交给UNFCCC的国家清单报告中包括自上而下估计的国家之一。从温室气体观测结果推断排放量的过程需要大量数据。为了了解所观测到的温室气体浓度的变化，科学家必须将来自不同环境和使用不同仪器的不同网络的联合收割机数据结合起来，了解传感器附近潜在源的分布和土地利用类型，并能够准确地模拟将温室气体从源输送到测量地点的大气过程。因此，到目前为止，温室气体数据的分析主要是针对个别研究论文进行的。在此，我们提出，需要云计算的新发展来帮助温室气体科学家克服温室气体网络整合和生产操作性更高分辨率的温室气体通量估计的一些主要障碍。我们将为英国温室气体数据科学“中心”创建基于云的框架。该中心将使用户（温室气体科学家，最终是公众）能够：-改善与温室气体数据提供商之间的信息流，因为云服务不在机构防火墙之后-将数据集处理成通用格式，然后可供全球用户访问（受任何所需的使用限制）-自动触发对新数据的操作，例如运行化学运输模型，这对解释温室气体数据至关重要-分析数据，模型输出和辅助信息（土地使用地图，排放清单等）在云端，无需个人用户下载数据集和运行模型（需要技术专长）-在基于网络的平台上可视化数据、模型和其他相关信息我们的团队在温室气体测量和分析、云计算和空间映射方面处于世界领先地位。该项目将在很大程度上依赖于云平台（作为EPSRC资助的BioSimSpace项目的一部分构建）和团队成员已经开发的温室气体分析代码库。这些工具构建在标准工具之上，如Quixyter Notebooks，分布式对象存储和无服务器功能。正是这些专业知识和这些开放的工具将使我们能够开发我们的数据科学中心的框架，该框架将在本项目结束时由温室气体研究人员进行扩展。我们设想这样一个中心可以成为英国庞大且不断增长的温室气体科学社区的中心，允许科学家在单一平台上上传，分析和可视化他们的数据，加强数据集成和群体之间的共享。最终，这个平台可以扩展到允许公众与温室气体数据互动，让他们了解英国的减排努力是否反映在大气观测中。

项目成果

Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

使用集成嵌套拉普拉斯近似和高斯马尔可夫随机场对痕量气体排放进行贝叶斯时空推断

• DOI: 10.5194/gmd-2019-66
• 发表时间: 2019
• 作者: Western L

Estimates of North African Methane Emissions from 2010 to 2017 Using GOSAT Observations

使用 GOSAT 观测数据估算 2010 年至 2017 年北非甲烷排放量

• DOI: 10.1021/acs.estlett.1c00327
• 发表时间: 2021
• 期刊: Environmental Science & Technology Letters
• 影响因子: 10.9
• 作者: Western L

Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

• DOI: 10.5194/gmd-13-2095-2020
• 发表时间: 2019-06
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: L. Western;Z. Sha;M. Rigby;A. Ganesan;A. Manning;K. Stanley;S. O'Doherty;D. Young;J. Rougier