the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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: final response (author comments only)
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RC1: 'Comment on amt-2021-31', Thomas von Clarmann, 23 Mar 2021
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
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
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CC1: 'Comment on amt-2021-31', Simone Ceccherini, 16 Apr 2021
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.
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RC2: 'Comment on amt-2021-31', Anonymous Referee #2, 25 May 2021
The authors propose to combine independent retrievals of CH4 from TROPOMI and IASI, which are in differing orbits, using well-known optimal estimation techniques. They compare the combined and individual L2 retrievals to a number of independent measurements while accounting for the individual sensitivities of the retrievals. Those comparisons are interesting as they show the strengths and weaknesses of IASI and TROPOMI.
They describe in detail the method to combine a column retrieval and a profile retrieval using optimal estimation techniques. This extends previous approaches, e.g., Luo et al, 2013, with a useful twist that could be applicable to other instrument combinations.
The value of this combined product, however, is not entirely clear. The theoretical analysis presented in the appendix neglects the fact that IASI is in a morning orbit and TROPOMI is in an afternoon orbit. The coincidence criterion that they propose is reasonable for evaluating L2 products against independent data but it is not adequate for actually combining data.
For this study, they need to directly account for the dislocation error, which could be modeled as a covariance. This could be done in a simulation context using, for example, CAMS methane (https://atmosphere.copernicus.eu/charts/cams/methane-forecasts).
This is important in part because the paper neglects a key question: who would want to use this data? Most scientists aren't interested in CH4 concentrations, they are interested in the fluxes that produce them. Given their characterization, IASI and TROPOMI can be used in this context. Models can readily account for the differences in time of day and how winds may shift the origins of morning and afternoon air parcels. The proposed method would improve DOFS but potentially at the expense of the anomalies that a model would exploit infer fluxes. They need to address this issue.
The paper asserts that a linear optimal estimation combination of L2 products is equivalent to a non-linear combination of L1B products. They never show this. Rather they depend on the mild non-linearity assumption in Rodgers, 2000. However, they never show that this assumption is valid for their problem. They could demonstrate it by showing in a simulation environment where they combine the L1B data in a multi-spectral retrieval and compare it to the equivalent L2 combination.
The authors are impressively unaware of the literature on combining satellite data for composition. They don't discuss the landmark Landgraf and Hasekamp (2007) or Worden et al, 2007 papers. In addition to Cuesta, there are a number of papers by Fu et al, 2013, 2018 that solve this problem with L1B data. Luo et al, 2013 demonstrate a similar approach for combining TES and MLS L2 data for CO. The authors are encouraged to familiarize themselves with the literature and cite appropriately.
As noted by the authors, the value of this approach will be better realized with Sentinel 5, rather than IASI and TROPOMI. I would suggest orienting the paper more towards a proof-of-concept for S5 or similar configurations, e.g, the A-Train. Whether this approach is as good as a combined L1B retrieval with coincident measurements or separately assimilating L2 products remains to be seen. But, the current strategy with IASI and TROPOMI is not clear.
Additional comments are embedded in the supplement.
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RC3: 'Comment on amt-2021-31', Anonymous Referee #3, 28 May 2021
The paper by Schneider et al. presents an interesting approach to derive tropospheric methane retrievals by synergism of IASI and TROPOMI level 2 products. The presented method is an a posteriori combination of the two level 2 products, using methane total columns from TROPOMI and profiles from IASI. The theoretical basis of the synergism is presented as well as comparisons with in situ measurements and ground-based retrievals. While previous work on fusion or synergism of level 2 products have been done, the present study shows an application of this kind of approach for methane retrievals, including the application and comparison with real data.
However, major revisions are needed in order to make the paper publishable. The present manuscript lacks some key elements to demonstrate the real contribution and validity of the combined retrieval and in some cases of criticism. Moreover, I disagree that the authors demonstrate the equivalency between this combination of level 2 products and a synergism of level 1 measurements. I strongly recommend modifying the manuscript with respect to the following major issues:
- Performance of the TROPOMI only approach vs the combined product: figure 3, 4 and 5 show that the TROPOMI total column retrievals are more sensitive (between 5 and 15 km), have less contribution of the a priori and less error than the combined product. How is this possible that the combination degrades the total column retrieval, both in sensitivity and the error? How is it possible that the IASI product does not really provide additional information according to the averaging kernels (except for a small change between 2-3 km of altitude, which is much smaller than the loss above) and it even slightly increase the error? Other synergetic satellite approaches show a very clear gain in total column retrievals, for example in total column degrees of freedom, as compared to single-instrument products. Why in the case of the combined product shown in this paper we do not see such gain and we even remark a small degradation? It is important to clarify the reason for this degradation and clearly indicate it in the manuscript. The manuscript should also point out limitations of the combined approach.
- Since the gain of the combination for the total column retrieval is not clear (even a small degradation is seen), what is the actual information provided by IASI measurements? And what would the advantage of the combined product as compared with a profile retrieval using TROPOMI measurements only?
- In Lines 8, 63-64 and 491-492, the authors strongly claim an equivalence between the level 2 combination and a level 1 synergism. I disagree that this statement is demonstrated in the paper. Such a strong statement can not only be based in theoretical estimations of appendix A2, but a practical comparison with real data should be given. The only way to demonstrate it is to fully develop and implement a full synergism of level 1 measurements of TROPOMI and IASI and then compare its performance with that of the a posteriori combination of level 2 products. However, the authors do not show such level 1 synergism product in their paper. I strongly suggest removing these statements, unless such thorough practical demonstration is provided.
- Global daily maps: The authors claim their product enables the generation of global daily maps of combined data (line 60), however the manuscript does not provide any map of the combined product. The validation of a satellite product in specific locations (figure 6) does not imply the capability to derive global daily maps with a satellite product. In order to provide such a general statement, it should be demonstrated by showing the capacity of this approach to map tropospheric methane distribution.
- Co-location of TROPOMI and IASI products: the authors arbitrary propose co-location criteria in section 2.2. Such criteria should be geophysical justified by comparison between the time-space variability of methane. What is the influence of the difference in the overpass time? How does it compare with the diurnal evolution of methane? The same in terms of space variability. It should also be clearly specified the horizontal resolution of the combined product and the coarser resolution of this product as compared to TROPOMI should be explicitly indicated as a limitation. Moreover, section 4.2 on data inconsistency should also deal with heterogeneity and time evolution of methane as observed by IASI and TROPOMI, and in addition to their biases.
- Title: “Synergetic use of IASI and TROPOMI space borne sensors ..” calls for ambiguity when it comes for the use of the IASI and TROPOMI spaceborne sensors measurements. I recommend to clearly indicate in the title “level 2 products” or “level 2 retrievals” instead of only the sensors.
- The authors claim that the major contribution of the combined product is a tropospheric column of methane that is not obtained with the individual single-instrument product. However, this statement should be more moderate since Figure 12 only show an increase in correlation R2 from 0.245 (IASI only) to 0.346 (combined product), which remains a rather moderate gain.
Other important points:
- A UTLS partial column retrieval from TROPOMI: in Figure 5c, black marks are drawn that would correspond to a TROPOMI retrieval. Does this partial column retrieval exist?
- The “correlation plots” or scatter plots of figures 7, 10 and 11 are very difficult to understand visually (biases and slopes) due to the change in the scales (vertical and horizontal). I recommend changing them to have the same range in both axes.
- The use of % for correlation coefficient is not the most common practice. I recommend expressing correlation coefficients as values between -1 and 1.
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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