DOI: 10.1016/j.atmosenv.2015.04.037
Scopus记录号: 2-s2.0-84929459047
论文题名: Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation
作者: Solazzo E ; , Galmarini S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 112 起始页码: 234
结束页码: 245
语种: 英语
英文关键词: AQMEII
; Hierarchical clustering
; KZ-filter
; Model evaluation
; Spatial representativeness
Scopus关键词: Fruits
; Low power electronics
; Ozone
; Stochastic models
; Stochastic systems
; Time series
; Uncertainty analysis
; AQMEII
; Hier-archical clustering
; KZ-filter
; Model evaluation
; Spatial representativeness
; Monitoring
; ozone
; atmospheric modeling
; atmospheric transport
; filter
; homogeneity
; model test
; monitoring
; ozone
; spatial distribution
; spectral analysis
; time series
; accuracy
; anisotropy
; Article
; environmental monitoring
; North America
; priority journal
; time series analysis
; Europe
; North America
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: A more sensible use of monitoring data for the evaluation and development of regional-scale atmospheric models is proposed. The motivation stems from observing current practices in this realm where the quality of monitoring data is seldom questioned and model-to-data deviation is uniquely attributed to model deficiency. Efforts are spent to quantify the uncertainty intrinsic to the measurement process, but aspects connected to model evaluation and development have recently emerged that remain obscure, such as the spatial representativeness and the homogeneity of signals subjects of our investigation. By using time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network the area of representativeness is firstly analysed showing, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal, suggesting the mere station classification to be not a suitable candidate to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed based on the spatial properties of the associativity of the spectral components of the ozone time series, in an attempt to determine the level of homogeneity. The spatial structure of the associativity among stations is informative of the spatial representativeness of that specific component and automatically tells about spatial anisotropy. Time series of ozone data from North American networks have also been analysed to support the methodology. We find that the low energy components (especially the intra-day signal) suffer from a too strong influence of country-level network set-up in Europe, and different networks in North America, showing spatial heterogeneity exactly at the administrative border that separates countries in Europe and at areas separating different networks in North America. For model evaluation purposes these elements should be treated as purely stochastic and discarded, while retaining the portion of the signal useful to the evaluation process. Trans-boundary discontinuity of the intra-day signal along with cross-network grouping has been found to be predominant. Skills of fifteen regional chemical-transport modelling systems have been assessed in light of this result, finding an improved accuracy of up to 5% when the intra-day signal is removed with respect to the case where all components are analysed. © 2015 Published by Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81739
Appears in Collections: 气候变化事实与影响
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作者单位: European Commission, Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Ispra, Italy
Recommended Citation:
Solazzo E,, Galmarini S. Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation[J]. Atmospheric Environment,2015-01-01,112