DOI: 10.1002/jgrd.50233
论文题名: BLUE-based NO <inf>2</inf> data assimilation at urban scale
作者: Tilloy A. ; Mallet V. ; Poulet D. ; Pesin C. ; Brocheton F.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 4 起始页码: 2031
结束页码: 2040
语种: 英语
英文关键词: data assimilation
; urban scale
Scopus关键词: Air quality
; Mean square error
; Nitrogen oxides
; Best linear unbiased estimator
; Concentration fields
; Data assimilation
; Ground observations
; Monitoring network
; Observation networks
; Root mean square errors
; Urban scale
; Computer simulation
; air quality
; atmospheric chemistry
; atmospheric modeling
; concentration (composition)
; data assimilation
; eddy covariance
; error analysis
; nitrogen dioxide
; parameterization
; roadside environment
; urban atmosphere
; Auvergne
; Clermont-Ferrand
; France
; Puy de Dome [Auvergne]
英文摘要: We aim at optimally combining air quality computations, from the Gaussian model ADMS Urban, and ground observations at urban scale. An ADMS simulation generated NO2 concentration fields across Clermont-Ferrand (France) down to street level, every 3 h for the full year 2008. A monitoring network composed of nine fixed stations provided hourly observations to be assimilated. Every 3 h, we compute the so-called BLUE (best linear unbiased estimator), which is a concentration field merging ADMS outputs and ground observations. Its error variance is supposed to be minimal under given assumptions regarding the errors on observations and model simulations. A key step lies in the modeling of error covariances between the computed NO2 concentrations across the city. We introduce a parameterized covariance which heavily relies on the road network. The covariance between two locations depends on the distance of each location to the road network and on the distance between the locations along the road network. Efficient parameters for the covariances are primarily chosen according to prior assumptions, χ2 diagnosis and leave-one-out cross-validations. According to the cross-validations, the improvements due to the assimilation seem moderately far from the observation network, but the root mean square error roughly decreases by 30-50% in the main city where the station density is high. The method is computationally tractable for the generation of improved concentration fields over a long period, or for day-to-day forecasts. Key PointsBLUE-based data assimilation is carried out at urban scale.Background error covariances are parameterized and depend on the road network.Cross validation shows 30% to 50% error decrease at urban stations. © 2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63906
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: INRIA, Paris-Rocquencourt Research Center, France; CEREA, Joint Laboratory École des Ponts ParisTech-EDF RandD, Université Paris-Est, Marne-la-Vallée, France; Numtech, Aubiére, France
Recommended Citation:
Tilloy A.,Mallet V.,Poulet D.,et al. BLUE-based NO <inf>2</inf> data assimilation at urban scale[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(4)