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.

可以使用计算要求的贝叶斯“反相反”方法来从其大气浓度的测量中推断出温室气体（GHG）的排放。布里斯托尔大学（UOB）和爱丁堡大学（UOE）的研究小组使用此信息来量化英国和其他国家的排放的大小和不确定性，b）确定自然大气变异性的驱动因素。 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成员）和d）国家地球观察中心。这项工作涉及两个阶段。首先，在将其输出与使用（通常）单节点数据分析系统进行排放验证的观察结果之前，将化学传输模型（CTMS；后者的统计技术涉及在极大的数组上使用CPU和内存密集的线性代数算法，这已经在推动了我们现有基础架构的限制。现在，DECC网络和量规内的活动提出了进一步的挑战：1）充分探索迅速增长的异质测量数据（数百万个数据点）； 2）使用这些数据以比以往任何时候都更高的分辨率来推断排放（例如，以1.5 km的水平分辨率在英国使用名称模型输出）。拟议的资产将有助于加强我们执行这项工作第二阶段的能力。

项目成果

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

• DOI: 10.1029/2020gl087822
• 发表时间: 2020-07
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: A. Ganesan;M. Manizza;E. Morgan;C. Harth;E. Kozlova;T. Lueker;A. Manning;M. Lunt;Jens Mühle-Jens-Mühl

Rapid increase in dichloromethane emissions from China inferred through atmospheric observations.

• DOI: 10.1038/s41467-021-27592-y
• 发表时间: 2021-12-14
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: An M;Western LM;Say D;Chen L;Claxton T;Ganesan AL;Hossaini R;Krummel PB;Manning AJ;Mühle J;O'Doherty S;Prinn RG;Weiss RF;Young D;Hu J;Yao B;Rigby M
• 通讯作者: Rigby M

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: 10.5194/acp-2021-548
• 发表时间: 2021
• 作者: Lunt M

A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME

用于拉格朗日粒子分散模型足迹的机器学习模拟器：使用 NAME 的案例研究

• DOI: 10.5194/egusphere-2022-1174
• 发表时间: 2022
• 作者: Fillola E

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

• DOI: 10.5194/acp-14-3855-2014
• 发表时间: 2014-01-01
• 期刊: ATMOSPHERIC CHEMISTRY AND PHYSICS
• 影响因子: 6.3
• 作者: Ganesan, A. L.;Rigby, M.;Weiss, R. F.
• 通讯作者: Weiss, R. F.