23 Feb 2021
23 Feb 2021
Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product
- 1Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
- 2Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, Karlsruhe, Germany
- 3Izaña Atmospheric Research Center, Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain
- 4Earth Science Group, SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
- 5Center for Isotope Research, University of Groningen, Groningen, The Netherlands
- 6Space and Earth Observation Centre, Finnish Meteorological Institute, Sodankylä, Finland
- 7Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, 91191 Gif sur Yvette, France
- 8LMD/IPSL, CNRS, Ecole polytechnique, University Paris-Saclay, Palaiseau, France
- 9Swiss Federal Laboratories for Materials Science and Technology (EMPA), Dübendorf, Switzerland
- 10Air Monitoring Network, Federal Environment Agency (UBA), Langen, Germany
- 11Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia
- 12National Institute of Water and Atmospheric Research Ltd (NIWA), Lauder, New Zealand
- anow at: Laboratory for the Analysis of Radiocarbon with AMS (LARA) Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP) & Oeschger Centre for Climate Change Research (OCCR), University of Bern, Switzerland
- bnow at: Deutscher Wetterdienst (DWD), Albin-Schwaiger-Weg 10, Hohenpeissenberg, Germany
- 1Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
- 2Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, Karlsruhe, Germany
- 3Izaña Atmospheric Research Center, Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain
- 4Earth Science Group, SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
- 5Center for Isotope Research, University of Groningen, Groningen, The Netherlands
- 6Space and Earth Observation Centre, Finnish Meteorological Institute, Sodankylä, Finland
- 7Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, 91191 Gif sur Yvette, France
- 8LMD/IPSL, CNRS, Ecole polytechnique, University Paris-Saclay, Palaiseau, France
- 9Swiss Federal Laboratories for Materials Science and Technology (EMPA), Dübendorf, Switzerland
- 10Air Monitoring Network, Federal Environment Agency (UBA), Langen, Germany
- 11Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia
- 12National Institute of Water and Atmospheric Research Ltd (NIWA), Lauder, New Zealand
- anow at: Laboratory for the Analysis of Radiocarbon with AMS (LARA) Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP) & Oeschger Centre for Climate Change Research (OCCR), University of Bern, Switzerland
- bnow at: Deutscher Wetterdienst (DWD), Albin-Schwaiger-Weg 10, Hohenpeissenberg, Germany
Abstract. The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors.
We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total column averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the upper tropospheric and lower stratospheric (UTLS) CH4 partial column information of the corresponding IASI product. As consequence, the combined product offers in addition sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effects are verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at six globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 24 individual AirCore soundings, and lower tropospheric CH4 data derived from continuous ground-based in-situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. We find indications of a weak positive bias of about +1 % of the combined lower tropospheric data product with respect to the references. For the UTLS CH4 partial columns we find no significant bias.
Matthias Schneider et al.
Status: open (until 02 May 2021)
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RC1: 'Comment on amt-2021-31', Thomas von Clarmann, 23 Mar 2021
reply
The authors of this paper are my colleagues and friends. Thus, I am not in a good position to provide an unbiased review. Due to this obvious conflict of interest, I would have preferred to provide neither a rating of the manuscript nor a recommendation, but the system forces me to do so. On request of the editor I have, however, checked the algebra in the Appendix. My rating of the scientific quality and my recommendation refer to the Appendix only.
The goal of this paper is to present a data fusion method that needs as input only data that are provided to the public, and thus can be applied by a wider community than the retrieval scientists involved in the level-2 processing of the parent data. The Appendix provides the theoretical basis for this.
My major concern is that in some places the derivations of the equations used seem to involve the inverse of a singular matrix. Such manipulations of equations are not truth-preserving. So I am afraid that it would need a major revision of Appendix A to remedy this. I have discussed this issue with the lead author, who meanwhile has found a theoretical basis which seems robust to me. Thus, I am confident that in the revision of the manuscript this defect will be remedied.
Some further issues:
Please note that the Sx matrix in Eqs A4 and A6 has a probabilistic interpretation only if Sa-1 is chosen as a regularization matrix. The text before A6 suggests that the authors consider also other constraint matrices. In this case, however, Sx has no probabilistic interpretation.
lines 602/603: I do agree that Kalman filtering and retrieval use the same mathematical formalism, but I do not find it so clear in the comparison between Eqs A2 and A10. Formalism A2 is based on Rodgers' n-form, while A10 is more related to Rodgers m-form; the equivalence is correct, but it is not so obvious from these equations. Note that A10 involves covariance matrices, while A2 involves inverse covariance matrices. There is nothing wrong with the algebra here, but I would suggest to change the text.
B2 is not quite correct; we need an L matrix with x-values down the diagonal, and a ^L matrix with ^x-values down the diagonal. With these B2 should read
A = ^L Al L-1≈ ^L Al ^L-1
I think that the approximative character of this transformation needs to be mentioned, and the `approximately equal' sign should be used.typo in line 661 "logarithmnic" -> "logarithmic"
B3 can cause trouble: an Sl matrix representing a symmetric pdf in the log-space cannot be mapped on an S-matrix representing a symmetric pdf in the linear space. Thus, l661 should read "can be expressed in linear approximation in the linear scale" and the equal-sign in B3 should be replaced by an approximativly equal sign.
Thomas von Clarmann
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AC1: 'Reply on RC1', Matthias Schneider, 29 Mar 2021
reply
Many thanks to Thomas for his detailed inspection of the Appendix. The respective discussions with him have been extremely helpful. Please find attached a revised Appendix that considers the major remarks of Thomas. In particular Appendix A2 has been comprehensively revised.
While writting this reply I get the note "please do NOT submit your revised manuscript here as supplement". I hope that I make no mistake by uploading the revised Appendix. My intention is simply to contribute to the discussion and show the other referees (and interested readers) the progress achieved so far during the discussion phase.
Best regards, Matthias Schneider
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AC1: 'Reply on RC1', Matthias Schneider, 29 Mar 2021
reply
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CC1: 'Comment on amt-2021-31', Simone Ceccherini, 16 Apr 2021
reply
I make my congratulations to the authors for this very interesting paper that presents a way to combine L2 products of different co-located measurements of the same species.
This comment is just to suggest them to cite Warner et al. (2014) that, from what I can understand, used the same method proposed by the authors of this paper to combine CO products of AIRS and TES as well of AIRS and MLS.
Then, as another method that uses the output of the individual retrievals to combine different co-located measurements, I suggest to cite the Complete Data Fusion method (Ceccherini et al., 2015).
I have demonstrated that the method used by the authors of this paper is equivalent to the Complete Data Fusion method in the sense that starting from the formula of one method we can obtain, using algebraic operations, the formula of the other method.
I have submitted the proof of this equivalence as a peer-reviewed comment to this paper to AMT in order that others can verify the proof as well. I hope that this peer-reviewed comment will be published in AMT Discussions as soon as possible, so that it is available also to the authors of this paper.
References:
Warner, J. X., Yang, R., Wei, Z., Carminati, F., Tangborn, A., Sun, Z., Lahoz, W., Attié, J.-L., El Amraoui, L., and Duncan, B.:
Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites, Atmos. Chem. Phys., 14, 103–114,
https://doi.org/10.5194/acp-14-103-2014, 2014.
Ceccherini, S., Carli, B., and Raspollini, P.: Equivalence of data fusion and simultaneous retrieval, Opt. Express, 23, 8476-8488, 2015.
Matthias Schneider et al.
Data sets
MUSICA IASI data Matthias Schneider, Benjamin Ertl, and Christopher J. Diekmann http://www.imk-asf.kit.edu/english/musica-data.php
TROPOMI data Tobias Borsdorff, Jochen Landgraf, and Alba Lorente ftp://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/ch4/14_14_Lorente_et_al_2020_AMTD/
TCCON data Divers, station dependent http://tccondata.org
GAW data Divers, station dependent https://gaw.kishou.go.jp/search/
Matthias Schneider et al.
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