Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
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欧拉大气化学输运模型不确定性评估和全局敏感性分析的先进方法
DOI：
10.5194/acp-19-2881-2019
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发表时间：
2018
影响因子：
6.3
通讯作者：
M. Heal
中科院分区：
文献类型：
--
作者：
Ksenia Aleksankina;S. Reis;M. Vieno;M. Heal
抽象的。大气化学输送模式(ACTM)被广泛应用于
为制定减排政策提供科学支持
空气污染对人类健康的有害影响和
生态系统。因此，有必要定量评估的水平
模型不确定性，并确定影响模型输入参数的因素
对不确定性的影响最大。对于复杂的基于流程的模型，如ACTM，
不确定性和全球敏感性分析仍然具有挑战性，而且
通常受到计算约束的限制，这是由于对大型
模型运行数。在这项工作中，我们演示了一种基于仿真器的方法
以不确定性量化和基于方差的敏感性分析为基础
EMEP4UK模式(欧洲监测和评价的区域应用
方案气象综合中心(西部)。单独的高斯型
使用过程仿真器估计未采样点的模型预测
对于每个建模的网格单元，在不确定的模型输入空间中。这个
模拟器的训练点是使用优化的拉丁语选择的
超立方体抽样设计。地表浓度的不确定度
臭氧、NO2和PM2.5的传播来自于
NOx、SO2、NH3、VOC和初级污染物的人为排放
英国国家大气排放清单报告的PM2.5。这个
年平均模型的EMEP4UK不确定度分析结果
预测表明，模拟的臭氧地表浓度，
NO2和PM2.5是电网中不确定性最高的
由城市区域组成的单元格(分别高达±7 %、±9 %和±9 %)。
臭氧和二氧化氮表面浓度的不确定性主要是氮氧化物排放的不确定性。
来自非主导部门(即不包括能源生产和
公路运输)和航运排放。此外，臭氧的不确定度
是由不包括的行业的VOC排放的不确定性推动的
使用溶剂。模拟的PM2.5浓度的不确定度为
主要由初级PM2.5排放和NH3的不确定性推动
农业部门的排放。不确定度和灵敏度分析
对于月平均模型，还对选定的五个格网单元进行了计算
用于说明不确定性大小的季节性变化的预测
以及不同模型投入对整体经济贡献的变化
不确定性。我们的研究证明了高斯过程的可行性
基于仿真器的不确定性和全局灵敏度分析方法，
这可应用于其他ACTM。进行这些分析有助于
增加对模型预测的信心。此外，仿真器
为这些分析创建的可用于预测ACTM响应
设置的范围内的扰动输入排放的其他组合
独创的拉丁超立方体抽样设计，无需重新运行ACTM，
从而允许快速探索性评估，大大减少了
计算成本。
Abstract. Atmospheric chemistry transport models (ACTMs) are extensively used to
provide scientific support for the development of policies to mitigate
the detrimental effects of air pollution on human health and
ecosystems. Therefore, it is essential to quantitatively assess the level of
model uncertainty and to identify the model input parameters that contribute
the most to the uncertainty. For complex process-based models, such as ACTMs,
uncertainty and global sensitivity analyses are still challenging and are
often limited by computational constraints due to the requirement of a large
number of model runs. In this work, we demonstrate an emulator-based approach
to uncertainty quantification and variance-based sensitivity analysis for the
EMEP4UK model (regional application of the European Monitoring and Evaluation
Programme Meteorological Synthesizing Centre-West). A separate Gaussian
process emulator was used to estimate model predictions at unsampled points
in the space of the uncertain model inputs for every modelled grid cell. The
training points for the emulator were chosen using an optimised Latin
hypercube sampling design. The uncertainties in surface concentrations of
O3, NO2, and PM2.5 were propagated from the uncertainties in
the anthropogenic emissions of NOx, SO2, NH3, VOC, and primary
PM2.5 reported by the UK National Atmospheric Emissions Inventory. The
results of the EMEP4UK uncertainty analysis for the annually averaged model
predictions indicate that modelled surface concentrations of O3,
NO2, and PM2.5 have the highest level of uncertainty in the grid
cells comprising urban areas (up to ±7 %, ±9 %, and ±9 %, respectively).
The uncertainty in the surface concentrations of O3 and NO2 were dominated by uncertainties in NOx emissions combined
from non-dominant sectors (i.e. all sectors excluding energy production and
road transport) and shipping emissions. Additionally, uncertainty in O3
was driven by uncertainty in VOC emissions combined from sectors excluding
solvent use. Uncertainties in the modelled PM2.5 concentrations were
mainly driven by uncertainties in primary PM2.5 emissions and NH3
emissions from the agricultural sector. Uncertainty and sensitivity analyses
were also performed for five selected grid cells for monthly averaged model
predictions to illustrate the seasonal change in the magnitude of uncertainty
and change in the contribution of different model inputs to the overall
uncertainty. Our study demonstrates the viability of a Gaussian process
emulator-based approach for uncertainty and global sensitivity analyses,
which can be applied to other ACTMs. Conducting these analyses helps to
increase the confidence in model predictions. Additionally, the emulators
created for these analyses can be used to predict the ACTM response for any
other combination of perturbed input emissions within the ranges set for the
original Latin hypercube sampling design without the need to rerun the ACTM,
thus allowing for fast exploratory assessments at significantly reduced
computational costs.
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DOI：
10.1016/j.cageo.2009.11.004
发表时间：
2010-06
期刊：
Comput. Geosci.
影响因子：
--
作者：
N. Urban;Thomas E. Fricker
通讯作者：
N. Urban;Thomas E. Fricker
影响因子：
6.3
作者：
Vieno, M.;Dore, A. J.;Sutton, M. A.
通讯作者：
Sutton, M. A.
影响因子：
5
作者：
A. Dore;D. Carslaw;C. Braban;M. Cain;C. Chemel;C. Conolly;R. Derwent;S. Griffiths;Jane R. Hall;G. Hayman;S. Lawrence;S. Metcalfe;A. Redington;D. Simpson;M. Sutton;P. Sutton;Y. S. Tang;M. Vieno;M. Werner;J. D. Whyatt
通讯作者：
A. Dore;D. Carslaw;C. Braban;M. Cain;C. Chemel;C. Conolly;R. Derwent;S. Griffiths;Jane R. Hall;G. Hayman;S. Lawrence;S. Metcalfe;A. Redington;D. Simpson;M. Sutton;P. Sutton;Y. S. Tang;M. Vieno;M. Werner;J. D. Whyatt
影响因子：
6.3
作者：
Lee, L. A.;Carslaw, K. S.;Spracklen, D. V.
通讯作者：
Spracklen, D. V.