scholarly journals Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

2008 ◽  
Vol 113 (D17) ◽  
Author(s):  
Jan Fokke Meirink ◽  
Peter Bergamaschi ◽  
Christian Frankenberg ◽  
Monica T. S. d'Amelio ◽  
Edward J. Dlugokencky ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Inverse Modeling ◽  
Methane Emissions ◽  
Variational Data Assimilation ◽  
Atmospheric Methane

scholarly journals Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

2008 ◽  
Vol 8 (21) ◽  
pp. 6341-6353 ◽  
Author(s):  
J. F. Meirink ◽  
P. Bergamaschi ◽  
M. C. Krol
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Full Potential ◽  
Atmospheric Methane ◽  
Assimilation System

Abstract. A four-dimensional variational (4D-Var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model.

scholarly journals Technical Note: Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

2008 ◽  
Vol 8 (3) ◽  
pp. 12023-12052 ◽  
Author(s):  
J. F. Meirink ◽  
P. Bergamaschi ◽  
M. C. Krol
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Full Potential ◽  
Atmospheric Methane ◽  
Assimilation System

Abstract. A four-dimensional variational (4D-var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model.

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