Preprints
https://doi.org/10.5194/amt-2021-98
https://doi.org/10.5194/amt-2021-98

  26 Apr 2021

26 Apr 2021

Review status: a revised version of this preprint is currently under review for the journal AMT.

Comment on “Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product”

Simone Ceccherini Simone Ceccherini
  • Istituto di Fisica Applicata “Nello Carrara” del Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy

Abstract. A great interest is growing about methods that combine measurements from two or more instruments that observe the same species either in different spectral regions or with different geometries. Recently, a method based on the Kalman filter has been proposed to combine IASI and TROPOMI methane products. We show that this method is equivalent to the Complete Data Fusion method. Therefore, the choice between these two methods is driven only by the advantages of the different implementations. From the comparison of the two methods a generalization of the Complete Data Fusion formula, which is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived.

Simone Ceccherini

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-98', Anonymous Referee #1, 05 Jul 2021
    • AC1: 'Reply on RC1', Simone Ceccherini, 28 Sep 2021
  • EC1: 'Comment on amt-2021-98', Russell Dickerson, 20 Aug 2021
  • RC2: 'Comment on amt-2021-98', Anonymous Referee #2, 23 Sep 2021

Simone Ceccherini

Simone Ceccherini

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Short summary
The equivalence between the data fusion performed using the Kalman filter and the Complete Data Fusion has been proved and a generalization of the Complete Data Fusion formula, that is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived. The two methods are also equivalent to the measurement space solution data fusion method and, for moderately non linear problems, the three methods are all equivalent to the simultaneous retrieval.