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A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

By Kumaresh Singh, Mohamed Jardak, Adrian Sandu, Kevin Bowman and Meemong Lee

Abstract

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content yields an analysis comparable in quality with the one obtained using the entire data set

Topics: Numerical Analysis
Year: 2012
OAI identifier: oai:vtcstechreports.OAI2:1196

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Citations

  1. (2007). A Bayesian tutorial for data assimilation. Physica D,
  2. (2009). A comparison of analytical and adjoint Bayesian inversion methods for constraining Asian sources of CO using satellite (MOPITT) measurements of CO columns.
  3. (2010). A hybrid approach to estimating error covariances in variational370 data assimilation.
  4. (2000). Adjoint implementation of Rosenbrock methods applied to variational380 data assimilation problems.
  5. (2005). Adjoint inverse390 modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment.
  6. (2005). Adjoint sensitivity analysis of regional air quality models.
  7. (2007). Autoregressive models of background errors for chemical data assimilation.
  8. (2006). Capturing time and vertical variability of tropospheric ozone: A study using TES nadir retrievals.
  9. (2004). Characterization of atmospheric profile retrievals from Limb450 Sounding Observations of an inhomogeneous atmosphere.
  10. (2003). Computational aspects of chemical data assimilation into atmospheric models.
  11. (2011). Construction of non-diagonal background error covariance matrices in global chemical data assimilation.
  12. (2008). Correlated observation errors in data assimilation.
  13. (2007). Development of the adjoint of GEOS-Chem.
  14. (2012). Discrete second order adjoints in atmospheric chemical transport modeling.
  15. (2007). Ensemble-based chemical data assimilation. I: General approach.
  16. Ensemble-based data assimilation for atmospheric chemical transport models.
  17. (2008). Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the Tropospheric Emission Spectrometer.
  18. (2007). Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements.
  19. Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation.
  20. (2009). Impact of the assimilation of ozone from the Tropospheric Emission Spectrometer on surface ozone across North America. Geophysical Research Letters,
  21. (2009). Implementation and evaluation of an array of chemical solvers in a global chemical transport model. Geophysical Model Development,
  22. (2009). Improving GEOS-Chem model forecasts through profile retrievals from Tropospheric Emission Spectrometer.
  23. (2004). Influence-matrix diagnostic of data assimilation system.
  24. Information content and optimization of high spectral resolution measurements.
  25. Intercontinental chemical transport experiment ozonesonde network study (IONS) 2004: 2. Tropospheric ozone budgets and variability over northeastern North America".
  26. (2009). Intercontinental source attribution of ozone pollution at western U.S. sites using an adjoint method. Geophysical Research Letters,
  27. (2009). Inverse modeling and mapping U.S. air quality influences of inorganic PM2.5 precursor emissions with the adjoint of GEOS-Chem. Atmospheric Chemistry and Physics,
  28. (2005). Inverse modeling of aerosol dynamics using adjoints: Theoretical and numerical considerations.
  29. (2004). Inverse modeling of aerosol dynamics: Condensational growth.
  30. (1997). L-BFGS-B: Algorithm 778, FORTRAN routines for large scale bound con-455 strained optimization.
  31. (2006). Measuring information content from observations for data assimilation: relative entropy versus Shannon entropy difference.
  32. (2008). On analysis error covariances in variational data assimilation.
  33. (1922). On the mathematical foundations of theoretical statistics.
  34. (2003). Potential of observations from the Tropospheric Emission Spectrometer to constrain continental sources of carbon monoxide.
  35. (2008). Predicting air quality: Im-360 provements through advanced methods to integrate models and measurements.
  36. (2007). S.Q.: Applications of information theory in ensemble data assimilation.
  37. (1989). The role of the Hessian matrix in fitting models to measurements. doi
  38. (2001). Tropospheric emission spectrometer for the Earth Observing System’s Aura satellite. Applied Optics,

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