Advanced computing architecture to support the estimation and reporting of UK GHG emissions

先进的计算架构支持英国温室气体排放的估算和报告

• 批准号: NE/L013088/1
• 负责人: Matthew Rigby
• 金额: $ 10.39万
• 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: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FL013088%2F1
• 关键词: Advanced; computing; architecture; support; estimation

Greenhouse gas (GHG) emissions can be inferred from measurements of their atmospheric concentration using computationally demanding Bayesian "inverse" methods. This information is being used by research groups at the University of Bristol (UoB) and the University of Edinburgh (UoE) to a) quantify the magnitude and uncertainty of emissions from the UK and other countries, and b) determine the drivers of natural atmospheric GHG variability. This work is underpins several major projects including: a) the Department for Energy and Climate Change (DECC) monitoring network, responsible for reporting UK GHG emissions to the United Nations Framework Convention on Climate Change, b) the £3m NERC-funded Greenhouse gAs Uk and Global Emissions (GAUGE) consortium (Palmer is PI, Rigby is co-I), c) the NASA and DECC-funded Advanced Global Atmospheric Gases Experiment (Rigby is member), and d) the National Centre for Earth Observation. This work involves two stages. Firstly, chemical transport models (CTMs; e.g. the UK Met Office NAME model) are run on multi-node clusters, before their output is compared to observations for emissions verification using (usually) single-node data analysis systems. The statistical techniques for the latter involve the use of CPU- and memory-intensive linear algebra algorithms on extremely large arrays, which are already pushing the limits of our existing infrastructure. Activities within the DECC network and GAUGE now pose further challenges: 1) to fully exploit a rapidly growing quantity of heterogeneous measurement data (many millions of data points); 2) to use these data to infer emissions at higher resolution than ever before (e.g. making use of NAME model output at a horizontal resolution of 1.5 km over the UK). The proposed assets will help to strengthen our ability to carry out this second stage of this work.

布里斯托大学 (UoB) 和爱丁堡大学 (UoE) 的研究小组正在使用计算要求较高的贝叶斯“逆”方法通过测量大气浓度来推断温室气体 (GHG) 排放量。 ) 量化英国和其他国家排放量的大小和不确定性，以及 b) 确定大气温室气体自然变化的驱动因素 这项工作支撑着几个主要项目，包括：a) 能源和气候变化部 (DECC) 监测网络。 ,负责向《联合国气候变化框架公约》报告英国温室气体排放量，b) NERC 资助的 300 万英镑温室气体英国和全球排放 (GAUGE) 联盟（Palmer 是 PI，Rigby 是 co-I），c) NASA和 DECC 资助的先进全球大气气体实验（Rigby 是成员），以及 d) 国家地球观测中心这项工作涉及两个阶段。 （CTM；例如英国气象局 NAME 模型）在多节点集群上运行，然后将其输出与使用（通常）单节点数据分析系统进行排放验证的观测结果进行比较。后者的统计技术涉及使用超大型数组上的 CPU 和内存密集型线性代数算法已经突破了我们现有基础设施的极限，现在 DECC 网络和 GAUGE 中的活动提出了进一步的挑战：1）充分利用数量快速增长的数据。异构测量数据（数百万个数据点）；2）使用这些数据以比以往更高的分辨率推断排放量（例如，利用英国上空 1.5 公里水平分辨率的 NAME 模型输出）。有助于增强我们开展第二阶段工作的能力。

项目成果

Marine Nitrous Oxide Emissions From Three Eastern Boundary Upwelling Systems Inferred From Atmospheric Observations

根据大气观测推断三个东部边界上升流系统的海洋一氧化二氮排放量

• DOI: http://dx.10.1029/2020gl087822
• 发表时间: 2020
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Ganesan A

Australian chlorofluorocarbon (CFC) emissions: 1960-2017

澳大利亚氯氟碳化合物 (CFC) 排放量：1960-2017 年

• DOI: http://dx.10.1071/en19322
• 发表时间: 2020
• 期刊: Environmental Chemistry
• 影响因子: 4.3
• 作者: Fraser P

Atmospheric observations consistent with reported decline in the UK's methane emissions, 2013-2020

大气观测结果与 2013 年至 2020 年英国甲烷排放量下降情况一致

• DOI: http://dx.10.5194/acp-2021-548
• 发表时间: 2021
• 作者: Lunt M

Atmospheric observations consistent with reported decline in the UK's methane emissions (2013-2020)

大气观测结果与报告的英国甲烷排放量下降一致（2013-2020 年）

• DOI: http://dx.10.5194/acp-21-16257-2021
• 发表时间: 2021
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Lunt M

Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California

表征化石燃料 CO 大气反演的不确定性

• DOI: http://dx.10.5194/acp-19-2991-2019
• 发表时间: 2019
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Brophy K