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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 6
Atmos. Chem. Phys., 10, 3131-3139, 2010
https://doi.org/10.5194/acp-10-3131-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 10, 3131-3139, 2010
https://doi.org/10.5194/acp-10-3131-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Mar 2010

31 Mar 2010

Technical Note: Variance-covariance matrix and averaging kernels for the Levenberg-Marquardt solution of the retrieval of atmospheric vertical profiles

S. Ceccherini1 and M. Ridolfi2 S. Ceccherini and M. Ridolfi
  • 1Istituto di Fisica Applicata "Nello Carrara" (IFAC) del Consiglio Nazionale delle Ricerche (CNR), Firenze, Italy
  • 2Dipartimento di Fisica, Università di Bologna, Bologna, Italy

Abstract. The variance-covariance matrix (VCM) and the averaging kernel matrix (AKM) are widely used tools to characterize atmospheric vertical profiles retrieved from remote sensing measurements. Accurate estimation of these quantities is essential for both the evaluation of the quality of the retrieved profiles and for the correct use of the profiles themselves in subsequent applications such as data comparison, data assimilation and data fusion. We propose a new method to estimate the VCM and AKM of vertical profiles retrieved using the Levenberg-Marquardt iterative technique. We apply the new method to the inversion of simulated limb emission measurements. Then we compare the obtained VCM and AKM with those resulting from other methods already published in the literature and with accurate estimates derived using statistical and numerical estimators. The proposed method accounts for all the iterations done in the inversion and provides the most accurate VCM and AKM. Furthermore, it correctly estimates the VCM and the AKM also if the retrieval iterations are stopped when a physically meaningful convergence criterion is fulfilled, i.e. before achievement of the numerical convergence at machine precision. The method can be easily implemented in any Levenberg-Marquardt iterative retrieval scheme, either constrained or unconstrained, without significant computational overhead.

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