Quality quantifier of indirect measurements (Abstract/Poster in convegno)

Type
Label
  • Quality quantifier of indirect measurements (Abstract/Poster in convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Alternative label
  • S. Ceccherini, B. Carli and P. Raspollini (2012)
    Quality quantifier of indirect measurements
    in Advances in Atmospheric Science and Applications ATMOS 2012, Bruges Belgio, 18-22 Giugno 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • S. Ceccherini, B. Carli and P. Raspollini (literal)
Note
  • Poster (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Fisica Applicata \"Nello Carrara\" del Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy (literal)
Titolo
  • Quality quantifier of indirect measurements (literal)
Abstract
  • The quantification of the quality of indirect measurements is an important issue in the design of atmospheric measurements where a choice among different proposed experiments has to be done with the purpose of maximizing the information about a target set of parameters. In order to optimize the design of single and coordinated atmospheric measurements it is essential to have a quality parameter able to characterize consistently both the single measurements and the result of data fusion of several measurements. We consider a quality quantifier, referred to as measurement quality quantifier (MQQ), that satisfies the additivity property for data fusion, which implies that the MQQ of the data fusion of two or more independent measurements is the sum of the MQQs of the individual measurements The MQQ is derived from the Fisher information matrix and quantifies in absolute way the quality of the observations with respect to the retrieved parameters independently of any constraint that can be used in the retrieval. Differently from other information quantifiers, such as the Shannon information content, it can be also defined in absolute terms for ill-posed inverse problems. The Fisher information matrix is calculated from the Jacobian matrix of the forward model and from the covariance matrix of the observations and in the case of an unconstrained retrieval it is equal to the inverse of the covariance matrix of the retrieved quantities. We demonstrate that for a constrained retrieval a combination of the covariance matrix and of the averaging kernel matrix of the retrieval is invariant to the constraint and is equal to the Fisher information matrix. Since the covariance matrix and the averaging kernel matrix are generally distributed together with the retrieval products, using this invariant the data user can calculate the Fisher information matrix, and from that the MQQ, quantifying the information that really comes from the observations and excluding the information coming from the constraints used to perform the retrieval. When the measured quantity is a continuous distribution (such as a vertical profile) the MQQ depends on the sampling grid of the distribution and the need arises of characterizing the observations independently of the selected grid. To this purpose we introduce the grid normalized MQQ that makes possible the comparison of quality of measurements represented on different grids. We use the MQQ to evaluate the quality of the measurements performed by the MIPAS instrument on board of Envisat. In particular we quantify the quality increase occurred when the measurement mode of MIPAS was changed from full to optimized resolution. (literal)
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