the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm
- 1Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan
- 2Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan
- 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 4Deutscher Wetterdienst, Meteorological Observatory Hohenpeissenberg, 82383, Germany
- 5Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, NSW 2522, Australia
- 6Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Germany
- 7Laboratoire d'Études du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA-IPSL), Sorbonne Université, CNRS, Observatoire de Paris, PSL Université, 75005 Paris, France
- 8Royal Belgian Institute for Space Agency (BIRA-IASB), Brussels, Belgium
- 9Space and Earth Observation Centre, Finnish Meteorological Institute, Tähteläntie 62, 99600 Sodankylä, Finland
- 10CNRC & LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 11National Institute of Meteorological Sciences, 33, Seohbuk-ro, Seogwipo-si, Jeju-do, 63568, Republic of Korea
- 1Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan
- 2Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan
- 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 4Deutscher Wetterdienst, Meteorological Observatory Hohenpeissenberg, 82383, Germany
- 5Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, NSW 2522, Australia
- 6Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Germany
- 7Laboratoire d'Études du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA-IPSL), Sorbonne Université, CNRS, Observatoire de Paris, PSL Université, 75005 Paris, France
- 8Royal Belgian Institute for Space Agency (BIRA-IASB), Brussels, Belgium
- 9Space and Earth Observation Centre, Finnish Meteorological Institute, Tähteläntie 62, 99600 Sodankylä, Finland
- 10CNRC & LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 11National Institute of Meteorological Sciences, 33, Seohbuk-ro, Seogwipo-si, Jeju-do, 63568, Republic of Korea
Abstract. The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO2 and XCH4) products (L2 products) obtained from the Greenhouse gases Observing SATellite for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O2 A, weak CO2, and CH4 bands of TANSO-FTS, although the residuals increase in the strong CO2 band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO2 and XCH4 over land. Further, the negative bias of XCO2 is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO2 growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96.
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Yu Someya et al.
Status: final response (author comments only)
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RC1: 'Comment on amt-2022-281', Anonymous Referee #1, 26 Nov 2022
This paper describes updates implemented in the V3 NIES GOSAT xCO2 and xCH4 algorithm. The changes from the previous V2 algorithm are mostly incremental, including retrievals of cirrus clouds (instead of screening), updated degradation model for the TANSO-FTS instrument, and updated solar irradiance and gas absorption coefficient tables. These updates lead to somewhat mixed results. As compared with the V2 algorithm, the fitting residuals of V3 appear to be smaller for O2-A, weak CO2, and CH4 bands, but larger for strong CO2 bands over the ocean. Over land, the number of retrievals passing through the quality control screening increases, without significant changes in the quality of the retrievals. Over ocean, the xCO2 retrievals from the V3 algorithm have a larger negative bias but more consistent growth rate as compared with in situ measurements. The paper is well-organized, and the figures and tables are mostly clear. The topic fits well within the scope of Atmos. Meas. Tech. On the other hand, the results could be more thoroughly discussed, and some details are missing. I would recommend that the paper be accepted for publication after major revisions.
Specific comments:
I wonder if the forward radiative transfer model has been updated since Yoshida et al. (2011). If so, please provide some discussion on that aspect of the algorithm.
Figs. 1-4: can the authors clarify if the residuals shown here were calculated before or after the post-processing quality control? Also, can the authors provide some metrics (other than visual inspection) based on which conclusions were drawn, for example, on the fitting quality for V2 and V3 in lines 160-165?
Can the authors provide more thorough analysis on the potential causes for the residuals in the strong CO2 band? Also, what measures are being considered to mitigate the negative bias in xCO2 over the ocean?
Figure 6: can the authors elaborate on what may have caused the changes in these parameters, in particular, AOT? Is there a time dependence in these changes?
Figure 9: can the authors provide some details on how the differences were calculated between in situ measurements and GOSAT? In particular, given that the surface and airborne in-situ measurements can be quite different, what measures were taken to ensure that a coherent time-series can be constructed?
Line 282: can the authors provide some details on how the growth rate were derived from a noisy time series (and the uncertainties of the growth rate)?
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RC2: 'Comment on amt-2022-281', T. E. Taylor, 29 Nov 2022
The paper provides updates on the NIES L2 Full Physics algorithm used to retrieval XCO2 and XCH4 from short-wave GOSAT measurements. A number of important updates have been made to increase the physical representation of the algorithm; treatment of cirrus clouds, solar model, instrument degradation model, and spectral absorption coefficients. The paper is well written and well organized and provides a useful update on the state of the algorithm. However, the results are very underwhelming and the level of detail in the analysis and discussion is lacking. Most of the conclusions in Section 4 (Results) do not show any supporting quantification. For example, line 162 claims "the fitting accuracies of V3 are found to be better than those of v2.9" This claim needs some fitting statistics to validate.Figures 1-4 (spectral residuals) need statistics. Also recommend writing out the name of each spectral band to each panel as a title or legend. Maybe adding a horizontal "zero" line would be helpful too. It could be informative to generate some "zoomed in" versions of the plots that focus on very specific spectral ranges so that the reader can get a better sense of the differences between the v2.9 and v3. Is it possible to change the vertical scale from absolute radiance units to percent of the continuum? Generally that is more intuitive and informative. Might be illustrative to list the XCO2 value from the retrievals for these specific cases in a legend. It would be somewhat interesting to see how much the XCO2 changed from v2.9 to v3.Does the NIES retrieval algorithm use EOFs to help account for mis-fitting the spectra caused by errors in absorption coefficients? If not, then perhaps it should be investigated.In Section 4.2, I would recommend that the authors show either a scatter plot of XCO2 from V3 vs v2.9 for matched soundings. Then apply a linear fit and report the statistics to demonstrate the difference. Alternatively, the delta XCO2 between the two versions could be plotted as a time series heat map. Are there differences in time?The maps in Fig 5 would be more useful if separate maps were made for land and ocean using different color scales. Are the current plots showing individual soundings or are these gridded values? I'm pretty sure that they are individual values, which means that there is probably a lot of overplotting in these figures, making them not all that useful. An alternative idea is to only plot a single year of soundings to reduce the amount of soundings overlap. Or plot seasonal maps (DJF, MAM, JJA, SON).Table 5 presents the statistics of the retrieved XCO2 for v2.9 and v3 as compared to TCCON measurements with the loose collocation requirements. These results essentially suggest that v3 XCO2 for landH is slightly worse compared to TCCON than it was for v2.9. It is probably worth investigating if this is driven by some of the TCCON sites in particular. Maybe a table showing the N/bias/scatter for v2.9 and v3 for each station individually would be useful. I notice that for the tighter collocation criteria, the overall statistics are slightly improved for v3 as compared to v2.9, which is good.I'm not really sure what to make out of the huge XCO2 bias in v3 over Ocean. It seems like some effort needs to go into the NIES L2FP to fix this by making the treatment of the ocean surface more complicated. Clearly the retrieval is unable to make a proper fit. Because of the delta XCO2 scale and the overplotting on Fig 5, it is impossible to tell what the large negative bias really looks like. Again, recommend making the maps separate for land/ocean with appropriate color scale. And probably more useful to show data for a single year/season at the beginning and end of the date record, e.g., 2010 vs 2020.Fig 7. Some fitting statistics are needed to support the discussion.Fig 8: the shading of the land is too light. Increase the contrast between land and ocean.Fig 9: Recommend plotting these as density heat maps, and making into 4 panels for Land/Ocean and v2.9/v3. Also needs fitting statistics to support discussion.Fig 10: Recommend separating into 2 panels for land/ocean. The current delta xco2 range of +/-10 ppm makes this plot very uninformative.Sec 4.4 Evaluating the long term trend using in situ measurements. This section needs some corresponding plots to support the discussion. There is a lot of good work here, but the authors do not show any of it. The paper is very short, so it wont hurt to add a few more interesting plots. Some plots of the calculated CO2 growth rate are definitely needed since that was one of the big motivations for the changes to the L2FP code.Sec 4.5 Bias correction. This section also needs one or two interesting plots to support the discussion.Line 55 in Introduction: please cite [Taylor, ESSD, 2022] for the ACOS L2FP retrieval as applied to GOSAT.Line 61/62. the sentence beginning with "However, the systematic structures..." should be split into 2 separate sentences for clarity.Line 92: how large is the expected increase in throughput due to this change? Did the change roughly meet expectations?Line 121; In the sentence "Although the new degradation model..." do you mean "Although the results from the new degradation model..."Line 176: "The temporal differences are possibly due to the contributions by the other components..." What other components specifically?Line 204; "...seems to correspond to that of the difference in XCH4 shown in Fig 5." Would be useful to generate and show a correlation plot to support this claim.Line 229: "There are no substantial changes...although the biases are different between v3 and v2.9 in some cases". Unfortunately the results are actually slightly worse for v3.Line 245/246: No statement about XCO2, which unfortunately is actually worse for v3 than v2.9 compared to TCCON.Line 319: "...a gap in the spectral baseline..." I dont understand this comment.
Yu Someya et al.
Yu Someya et al.
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