DOI: 10.1002/jgrd.50654
论文题名: Background error covariance estimation for atmospheric CO<inf>2</inf> data assimilation
作者: Chatterjee A. ; Engelen R.J. ; Kawa S.R. ; Sweeney C. ; Michalak A.M.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 17 起始页码: 10140
结束页码: 10154
语种: 英语
英文关键词: atmospheric CO2
; background error covariance matrix
; GOSAT CO2
; NMC method
; spatial and temporal CO2 variations
; variational data assimilation
Scopus关键词: Atmospheric composition
; Covariance matrix
; Data processing
; Error statistics
; Errors
; Estimation
; Information filtering
; Models
; Quality control
; Uncertainty analysis
; Value engineering
; Weather forecasting
; Background-error covariances
; GOSAT CO2
; NMC method
; spatial and temporal CO2 variations
; Variational data assimilation
; Carbon dioxide
; atmospheric pollution
; carbon dioxide
; carbon emission
; covariance analysis
; data assimilation
; error analysis
; flux measurement
; forecasting method
; GOSAT
; greenhouse gas
; prediction
; satellite imagery
; spatiotemporal analysis
; weather forecasting
; Europe
英文摘要: In any data assimilation framework, the background error covariance statistics play the critical role of filtering the observed information and determining the quality of the analysis. For atmospheric CO2 data assimilation, however, the background errors cannot be prescribed via traditional forecast or ensemble-based techniques as these fail to account for the uncertainties in the carbon emissions and uptake, or for the errors associated with the CO2 transport model. We propose an approach where the differences between two modeled CO2 concentration fields, based on different but plausible CO2 flux distributions and atmospheric transport models, are used as a proxy for the statistics of the background errors. The resulting error statistics: (1) vary regionally and seasonally to better capture the uncertainty in the background CO2 field, and (2) have a positive impact on the analysis estimates by allowing observations to adjust predictions over large areas. A state-of-the-art four-dimensional variational (4D-VAR) system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to illustrate the impact of the proposed approach for characterizing background error statistics on atmospheric CO 2 concentration estimates. Observations from the Greenhouse gases Observing SATellite "IBUKI" (GOSAT) are assimilated into the ECMWF 4D-VAR system along with meteorological variables, using both the new error statistics and those based on a traditional forecast-based technique. Evaluation of the four-dimensional CO2 fields against independent CO 2 observations confirms that the performance of the data assimilation system improves substantially in the summer, when significant variability and uncertainty in the fluxes are present. Key Points Difference in modeled CO2 fields is used to define background errors in CO2-DABoth atmospheric transport & flux pattern differences impact background errorsEvaluation using independent data shows positive impact on analysis estimates ©2013. American Geophysical Union. All Rights Reserved. 资助项目: NNX12AB90G
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63324
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: Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, United States; Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States; Data Assimilation Research Section, NCAR, Boulder, CO, United States; European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; NASA Goddard Space Flight Center, Greenbelt, MD, United States; Earth System Research Laboratory, NOAA, Boulder, CO, United States; Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO, United States
Recommended Citation:
Chatterjee A.,Engelen R.J.,Kawa S.R.,et al. Background error covariance estimation for atmospheric CO<inf>2</inf> data assimilation[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(17)