the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The GeoCarb greenhouse gas retrieval algorithm: Simulations and sensitivity to sources of uncertainty
Gregory R. McGarragh
Christopher W. O'Dell
Sean M. R. Crowell
Peter Somkuti
Eric B. Burgh
Berrien Moore III
Abstract. The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb are to advance our knowledge of the carbon cycle, in particular land-atmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4), and the effects on these fluxes on the Earth's radiation budget. GeoCarb will retrieve column integrated amounts of CO2 (XCO2), CH4 (XCH4) and CO (XCO; important for understanding tropospheric chemistry), in addition to Solar-Induced Fluorescence (SIF), which is proportional to the photosynthetic activity of vegetation, from hyperspectral resolution measurements in the O2 A-band at 0.76 um, the weak CO2 band at 1.6 um, the strong CO2 band at 2.06 um, and a CH4/CO band at 2.32 um. Unlike it's polar orbiting predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb will be in a Geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high spatial resolution imaging capabilities will provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land-atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes.
This paper will present a description of the GeoCarb instrument and the level-2 retrieval algorithms which will be followed by simulation experiments to determine a relatively comprehensive error budget for each target gas. Several sources of uncertainty will be explored including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to, forward model parameters including that from meteorology and spectroscopy. The results indicate that the errors (1σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMS errors of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO, like SIF, are dominated by measurement noise.
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Gregory R. McGarragh et al.
Status: final response (author comments only)
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RC1: 'Comment on amt-2023-17', Anonymous Referee #1, 26 Jun 2023
This paper describes the algorithm for retrievals of column averaged concentrations of CO2 (XCO2), CH4 (XCH4), and CO (XCO) from GeoCarb. The authors provided an overview of the GeoCarb instrument and some example measurement (scanning) modes. They also described the level 2 retrieval system that is largely based on the ACOS algorithm for OCO-2/3 XCO2 retrievals, including pre-processors for aerosol/cloud filtering, a full physics optimal estimation algorithm, and a post-processing step for data filtering and bias correction. Through several sensitivity tests using simulated radiance data, the authors demonstrated that the anticipated GeoCarb retrieval errors could meet the mission precision requirements for all three trace gases, although some potential uncertainty sources (e.g., stray light) were not considered in those tests. Based on the test results, the authors suggested that errors in XCO2 and XCH4 retrievals were dominated by systematic errors, whereas noise-driven errors were more important for XCO. Overall, this is a comprehensive simulation study on the retrieval performance of a (now unfortunately canceled) future greenhouse gas satellite mission. The paper is well organized, the discussion is detailed, and the retrieval methodology is well established in previous studies. The topic should be of interest to the readers of Atmos. Meas. Tech. I would recommend that the paper be accepted for publications after mostly minor revisions. However, I do feel that the presentation quality of the paper needs to be improved –there are many typos and grammatical errors that can be corrected if the authors can carefully proofread the manuscript.
Specific comments
Abstract: perhaps add the caveat that several other potential uncertainty sources are not included in the present study.
Page 2, line 1: please elaborate what you mean by “secondary changes to the Earth’s climate”.
Page 2, line 9: how do you define the accuracy of spatial and temporal scales?
Page 3, Line 14: one may argue that a constellation of LEO satellites can provide some temporal resolution – I wouldn’t say it is “not possible with polar orbiters”.
Page 4, line 30: the precision, as defined in the mission requirements, appears to be different from the simulation results shown later in the paper. Can the authors comment on the differences in the definition and how that may affect the interpretation of the results?
Figure 1: please check the unit for radiances.
Page 12, line 8: some of the spectral ranges listed here are less than twice the FWHM? Is there concern that this may lead to some inaccuracies in the convolved spectrum?
Section 3.3: can the authors comment on the a priori profiles for relatively short-lived CO (and to a lesser extent, CH4)? Will the a prioir profiles adequately represent those over major source regions such as major cities?
Table 4, for SIF mean, the a priori is 0 in the table, but Page 15, line 7 states that the priori SIF mean is from GASBAG. Please clarify. Also please check for consistency between the text and the table.
Section 3.5: does the A-band preprocessor algorithm produce SIF retrievals for OCO missions? If that’s the case, what is the reason behind using GASBAG for official SIF retrievals for GeoCarb?
Page 17, lines 21-24: this sentence is hard to understand.
Section 4.1: can the authors comment on the impact of different SSPs (anticipated vs. simulated) on retrieval throughput and retrieval errors?
Eq. 16: please check for accuracy.
Page 20, line 14: figure 4 shows AMF greater than 4.2?
Table 6: the table states that Runs # 10 and 11 have all perturbations in 4-9 but page 26 states that target shift (Run #7) is excluded in the kitchen sink tests. Please clarify.
Page 25, line 19: is “LS scaling factor” the same as “ILS stretch” in Table 4?
Eq. 19: please check for accuracy.
Page 27, line 1: is Xgas the retrieved column or profile? Please clarify.
Page 27, line 2: the forward model for the simulator uses 61 levels in the atmosphere but here the interpolation is to 72 levels – please clarify.
Figure 5: it is a bit confusing – from page 23 I’m under the impression that there are ~33000 soundings. Yet the number of raw soundings is far smaller at ~12000 for the aerosol/cloud free case. Does this mean that the pre-filtering is excluding a large number of aerosol/cloud free scenes as well?
Figure 7: can the authors comment more on the spatial pattern of the errors and how that may affect, for example, flux estimates? The text describing the figure (page 30, lines 4-8) is also somewhat inconsistent with the figure. For example, CH4 appears to be showing a positive bias at high latitudes in the map, but the text points to a negative bias.
Figure 8: the figure is informative but very complex. Perhaps it can be simplified. The standard deviation and mean after bias correction are not discussed much in the text.
Table 7: with dust AOD as a filter, is there concern that this may lead to sampling bias for certain dust-laden areas – since dust distribution is quite inhomogeneous?
Page 36 line 15: could you please update the reference for Crowell et al. 2021 in the reference list?
Page 37, line 3: the last sentence of the paragraph needs to be re-written.
Section 5.7: please explain what “hand-tuned” means here.
Table 9: please indicate how the component error is calculated.
Citation: https://doi.org/10.5194/amt-2023-17-RC1 -
RC2: 'Comment on amt-2023-17', Anonymous Referee #2, 28 Jun 2023
Review of “The GeoCarb greenhouse gas retrieval algorithm: Simulations and sensitivity to sources of uncertainty” by McGarragh et al., amt-2023-17
This paper presents a study for the GeoCarb mission, in which the authors describe the Level-2 retrieval algorithm for XCO, XCO2 and XCH4 and they conduct simulations to study error budgets for those three different trace gas retrievals in an effort to determine which sources of uncertainty play dominant roles in the total Level-2 error budget. This is achieved by running the Level-2 algorithm on synthetic radiance spectra generated with full knowledge of the atmospheric/radiative “truth” in the forward simulation. The inversion of these L1 radiances is then carried out with perturbations introduced into L2-algorithm input data like spectroscopic databases, meteorological priors; or perturbations added to the L1 spectra (e.g. from an instrument noise model). The results suggest that GeoCarb would have been able to meet retrieval precision requirements after bias correction.
The manuscript fits into the scope of AMT, the methods are valid and the results support the conclusions. However, the paper currently reads more like an internal, technical report and less like a scientific publication: it is very long, does not clearly emphasize what the novel concepts/results are - as compared to the existing literature on GeoCarb and the ACOS algorithm - and typos and imprecise language occur frequently throughout the text. While quality and clarity of the overall presentation must be improved, I also have some general questions about the methodological approach and the results.
General Comments
G1: This paper is quite similar to Polonsky et al. (2014), O’Brien et al. (2015) and O’Dell et al. (2012). As of now, a reader has to go to those articles to find out how they are different with respect to the methods and the results. The authors need to outline more clearly the novel aspects of their manuscript with respect to previous literature and make clear what new science the community can learn here. The main message currently seems to be that GeoCarb would have been able to meet mission requirements. This had already been confirmed by Polonsky et al. (2014). The authors need to explain what the added value of this publication would be.
G2: The paper is too long. Shorten by at least 5 pages to make this manuscript more accessible to readers. Here are some starting points for reducing the length: Much of chapter 3 can be found in previous works related to GeoCarb and OCO-2 and that content may be condensed. Similarly, the polarization experiment is a repeat of a previous study and it does not yield new conclusions. It can be left out of this manuscript.
G3: The bias correction (section 5.1.2) plays a very important role in this work, but as far as I can tell, the actual formalism is never introduced. How does the bias correction work?
G4: I find the discussion of the baseline experiment (Section 5.1) not satisfying from a scientific point of view. This is the simplest simulation experiment you carry out and you have knowledge of “the truth”. Biases that remain after bias correction are not explained and understood. You speculate about the roots causes for these results (“may be due”, “likely due”), but the reader cannot really understand what is going on here. What is the influence of the bias correction on the observed biases? It could be helpful to show a version of Figure 7, but for the clear-sky results (Figure 5). Is poor cloud filtering really the cause for the XCO bias over the Amazon? Wouldn’t it then be worth to build a cloud filter that works better? You mention altitude dependent biases, but from section 3.1 I understood that altitude knowledge is perfect in this experiment. Please study your baseline experiment more carefully to convince readers that you understand what is driving biases. A more thorough discussion of the retrievals performed on the clear-sky ensemble of spectra may be illustrative.
G5: The manuscript is written as if the GeoCarb mission had not been defunded last year. The authors should clearly indicate that the proposed instrument has been cancelled, unfortunately.
Specific Comments
The abstract should focus more on what the novel concepts or results of this study are. The first paragraph should be condensed.
Abstract, Line 4: XCO2, XCH4, XCO are introduced as column integrated amounts here, while on page 3, line 20 they are referred to as column integrated concentrations. I assume that in both cases the authors are referring to dry-air column averaged mole fractions. Please clarify in the abstract and throughout the text.
Abstract, Line 6: “hyperspectral” may be confusing for some readers, because it is often used to refer to coarse spectral resolution measurements with broad spectral coverage. Why not include the nominal FWHM of the different bands in the abstract to be more specific? “Hyperspectral” on Page 2, line 32 may be confusing in the same way.
Abstract, Line 7: OCO-3 is not a polar orbiting instrument
Abstract, Line 13: “relatively” is not very precise language, please elaborate.
Page 2, Line 2: Why not more recent IPCC report as reference?
Page 2, Line 25: remove reference O’Brien et al., 1998: it is unrelated to greenhouse gas surface fluxes and the context of this sentence.
Page 2, Lines 29-31: MOPITT does not use any of the spectral bands described in the previous sentence and it also makes use of thermal spectra; not just NIR/SWIR spectra. Please rewrite and consider using Deeter et al., 2003 (https://doi.org/10.1029/2002JD003186 ) as a reference instead of/in addition to Deeter et al., 2004.
Page 2, Line 34: perhaps update the GOSAT-2 reference with a more recent publication, e.g. Suto et al., 2021 - https://doi.org/10.5194/amt-14-2013-2021
Page 3, Lines 1-3: add Sentinel-5 to list of future missions.
Page 3, Lines 29-31: Reword so readers won’t assume Bril et al and Butz et al are modifications of WFM-DOAS.
Page 3, Line 34 - Page 4 Line 1: Not all of these algorithms are optimal estimation retrieval algorithms, for instance, some are based on Phillips-Tikhonov regularisation -> adjust wording.
Page 4, Line 8: what does “relatively comprehensive” mean?
Table 1: Where are these requirements from? Please provide details in the text.
Page 8, Lines 6, 8: detector persistence is not an optical “aberration”. Rephrase this also on page 22, Line 19 and page 42, line 19.
Page 8, second paragraph: You state that you are aware of existing instrument issues related to keystone/smile, stray light, etc.. The reader is left to wonder what extent these problems might have. You are proposing conclusions on GeoCarb’s potential performance later on in the paper without taking these effects into account, while these effects and the ability to correct for them, may definitely impact your results. Please add more context.
Page 10, Line 15: Why are you using an outdated HITRAN version? Same question for page 26, second paragraph.
Page 10, Line 29/30: Why are you not using the 2020 version of the solar spectrum?
Page 11, Lines 18-19: At this point in the GeoCarb mission, have the Stokes coefficients been determined? If so, what are they? O’Brien et al. (2014) mention that calibration efforts were in planning when their paper came out.
Page 11, Lines 19-22: to support this sentence it would be great to present the coefficients here.
Page 12, Lines 18-20: Why did you chose to use the mean noise values, instead of the actual noise levels across the spectral ranges (they appear to be known)? What does “roughly quadratically” mean? Be specific.
Table 3: Why are these values different from the ones in Table 2 of Polonsky et al. 2014?
Page 12, Lines 13-15: The reference Kumer et al. (2013) only deals with one band relevant to GeoCarb, namely the CH4 band. Add references and context so readers can understand if the noise model is based on measurements, and which ones.
Page 13, Lines 33-34: Please add a reference or explain why you expect the ILS stretch factor to mitigate effects from scene heterogeneity. I thought scene heterogeneity would mostly introduce asymmetry into the ILS. This would not be corrected for in your set up.
Page 13, Line 12: what does “nearly identical” mean?
Page 14/Table 4: Clearly indicate changes with respect to previous algorithm versions here.
Page 16, Lines 6-7: How did you determine the minimum Chi2 value possible and how did you chose the dP and Chi2 thresholds for cloud filtering?
Page 16, 3.5.2: What is the advantage of using the GASBAG retrieved gas ratios, instead of retrieving these yourself?
Page 16, Lines 18-19: What are the thresholds used for the gas ratios?
Page 19, Line 7: Why 78.06 degrees as upper limit? The plane-parallel assumptions breaks down at 70 degrees; solar zenith angle could/should be a filter criterion.
Page 21, Line 21: properly cite and acknowledge the carbon tracker work: https://gml.noaa.gov/ccgg/carbontracker/CT2017/citation.php
Page 21, Line 21: Explain why you chose to use Carbon Tracker instead of GGG2020/GEOS to create the trace gas profiles in the simulator. For some simulator inputs you use the same information as what you use for inputs into the retrievals and for some you use different inputs.
Page 21, Line 23: Which ECMWF model are you using? Please add a citation.
Page 21, Line 27: Add citation or link with which readers can access the CALIPSO data product you used; not just the algorithm design paper.
Page 22, Line 8: Which of the three kernels did you actually use?
Page 22, line 15: “solution used in the simulator is essentially the same to…” is it the same or not? Be specific.
Page 22, Line 25: what is a “reasonably realistic error budget”?
Page 22, Line 33: Why do you use GASBAG to filter for clouds instead of L2 variables?
Page 23, Lines 20-21: On pg. 10, line 27 you write that you use the quadratic term. Please clarify.
Page 23, Lines 21-22: Are the L1B spectra modelled with spectral baselines that are just linear functions of wavelength? If yes: I think this is too simplistic. Please discuss in the text.
Page 23, Lines 32-33: How big are the differences between the prior/truth CO2/CH4/CO?
Page 23, Line 33: What are the “subtle differences” in the radiative transfer? Be specific.
Page 25, Lines 24-25: The reader does not know how this experiment differs specifically from O’Brien et al. (2015). Please elaborate what the differences are and why this experiment needs to be repeated. Remove “…and GeoCarb instrument model”, as the noise model is irrelevant here, since the experiment does not consider the radiance noise from the instrument model.
Page 25, Line 29: If I understand correctly from page 29, the simulation runs on pixels the size of roughly 50x50 km^2? Why do you shift by 1 km? Please explain in the text. Maybe mention the pixel size here.
Page 26, Lines 4-6: The reader may not necessarily know what the actual differences are between the two models.
Page 26, Line 12: “T-width” - do you mean Temperature-dependence?
Page 26, Line 13: “speed dependence” - which line profile are you fitting?
Page 27, Line 17: “optimal”: rephrase. Optimal averaging kernels would be 1 everywhere.
Page 28, Line 1-2: Please add a citation to your statement that CO mixing ratio typically increases with height. I am not sure that is accurate (see e.g. www.atmos-chem-phys.org/acp/5/2901/).
Page 28, Line 30: It would be worthwhile putting these numbers in the context to previous simulation studies using the ACOS algorithm, for instance O’Dell et al., AMT, 2012. They also did this baseline experiment and found the precision for OCO-2 XCO2 to be 0.25 ppm. Why do you find larger standard deviation in the present experiment? Does the bias go the other way now? Why? What geophysical variables impact the baseline experiment (surface pressure?)? Additionally, Xi et al., AMT, 2015, doi:10.5194/amt-8-4817-2015 should be cited and discussed for reference.
Page 30, Lines 2-3: I agree that the bias is “curious”. Instead of speculating about the cause of the bias it would be valuable to actually confirm where this bias comes from. This sentence currently may give the impression that you observed a large bias, but did not bother investigating further, since the bias correction somehow helps you out.
Page 30, Line 6: Please remove the word “mild”.
Page 30, Figure 7: How are data aggregated here (spatially and temporally)? Please explain in the text.
Page 30, Line 15: When using the H2O ratio from GASBAG for cloud and aerosol screening here: What is the threshold here vs. in the prescreening, section 3.5?
Page 30, Line 19: “errors”: these are not “errors”, but low values in P_hat reflect the true actual shorter light path. Rephrase.
Page 31, Line 3: “increased” over what?
Page 31, Line 5: what are “relatively large” ice crystals?
Page 31, Line 5: Is there a significant difference in scattering efficiency for ice particles between the strong CO2 band and the 2.3 um band? Add a reference.
Page 31, Lines 10-13: Since you know the truth: be specific and quantify the error in prior surface pressure in high altitude locations where bias is observed.
Page 31, Line 10-13: What is the pre-screening signal threshold to flag ice/snow?
Page 31, Lines 13-14: Does the ILS fit in other bands also show this behaviour? What does the ILS fit do in the spectroscopy experiment?
Page 31, Line 17: Why italic and when is an experiment “unusual”?
Page 31, Line 21: When is a filter “sufficient”?
Page 32, Figure 8: dP-from-ABP plot: Why does light path enhancement, as seen here, lead to negative XCO2 bias - just as light path shortening? This seems unphysical to me. Please double check your results and explain in the text. The same can also be observed in the dP-from-L2 plot. In contrast, the CO2-grad-delta plot shows the behavior that I would expect, where light path enhancement and shortening lead to opposite signs in bias.
Page 33, Table 7: it appears that the last three variables in the bias correction have no significant impact on the performance of the correction. Why did you choose to include them anyway?
Page 33, Table 7: Why did you choose not to include albedo as a bias correction variable? How helpful would that be? Please discuss.
Page 33, Lines 10-11: Why such large underestimation of the uncertainty for XCH4? -> weaker spectral signatures?
Page 33, Lines 11-13: Please discuss why XCO errors are mostly noise driven?
Section 5.1.3: Please put your conclusions here into the context of previous work. i.e. is it a novel conclusion that XCH4, XCO2 are mostly affected by systematic errors and XCO more by noise? Is this generally true for satellite remote sensing missions targeting these gases?
Page 35, Lines 2-3: How was this new filter built?
Page 35, Section 5.3: Have you tried fitting a radiometric offset for this experiment?
Page 35, Section 5.3: What is the mechanism by which the bias correction is able to correct for errors caused by this experiment, given that the correction does not include albedo.
Page 37, Line 1: “largely consistent” -> what are the differences? Please explain.
Page 37, Line 6: What are the “three other days”?
Page 38, section 5.7: Why does XCH4 raw respond differently to this experiment than XCO2? Qualitatively it appears from Fig. 15 that the meteorological perturbation impacts these two column quantities in very different ways.
Page 39, Line 5: “All three target gas species still meet our mission requirements” Add “after bias correction.”
Page 39, Line 8: What does “roughly consistent” mean?
Page 41, Line 4-5: What are “real errors” and “good quality fractions”?
Page 41, Line 14: “Retrievals of […] are driven…” -> “Errors of […] retrievals are driven…”
Page 42, Line 1: see above: “Retrievals” -> “Errors”.
Page 42, Line 2: “GeoCarb will do amazingly well” extremely colloquial language. Rephrase.
Page 42, Line 3: “The retrievals of XCO2 , XCH4 , and XCO meet the mission precision requirements for all error sources, alone and in combination.” -> “The bias corrected retrievals of ….”
Technical Corrections
Abstract, Line 2: “…objectives of GeoCarb are…” -> “…objectives of GeoCarb were…”
Abstract, Line 8: Geostationary -> geostationary
Abstract, Line 8: “GeoCarb will be…” -> GeoCarb would have been…
Abstract, Line 12: “L2 algorithms”: several algorithms?
Abstract, Line 15: “…in addition to,…” -> “…in addition to…”
Abstract, Line 20: remove “like SIF” so that readers do not think that SIF retrieval errors are investigated here.
Page 2, Line 3: “…sources including,…” -> “…sources including…”
Page 2, Line 25: “It turns out that there is more signal relative to greenhouse gas surface fluxes…”: adapt wording of this sentence, because this sentence reads as if the fluxes are directly inferred from spectral measurements.
Page 2, Line 30: “…to measure of CO…” -> remove of.
Page 3, Line 1: verb is missing in sentence
Page 3, Lines 11-17: Change wording to reflect that GeoCarb has been defunded. (“The mission was a collaboration…”)
Page 3, Line 20: concentrations -> dry-air mole fractions?
Page 3, Line 33: “This is especially in the case” add true
Page 4, Line 6: why italic font?
Page 4, Line 8-9: “…have been been previous discussed…” -> “…have been previously discussed…”
Page 4, Line 31: move citation to end of sentence.
Table 2: correct “CH4$_4$” in the last row
Page 5, Line 13: “GeoCarb is the first planned…” -> “GeoCarb was the first planned…”
Caption of Figure 1: “Sample spectrums” -> “Simulated spectra”; add “.” at end of sentence
Page 8, Line 21: add “.” at end of sentence
Page 11, Equation 9: Add reference to the respective equation in O’Brien et al. (2014) and double check if it should be (Qcos2phi - Usin2phi) or (Qcos2phi + Usin2phi).
Page 16, Line 8: to thick -> too thick
Page 17, Line 14: \citet{Keely} -> \citep{Keely}
Page 18, Line 7: CSU acronym has not been introduced before; spell out.
Page 20, Line 19: “…GeoCarb will ultimately…” -> “…GeoCarb would have been…”
Page 21, Line 32: exists -> exist
Page 22, Line 8: isotopic -> isotropic
Page 22, Line 18: spectrums -> spectra
Page 22, Line 31: “L1B” -> “L2” ?
Page 23, Line 7: “…GeoCarb will…” -> “…GeoCarb was not meant to…”
Page 23, Line 19: “For aerosol. the same…” remove “.”
Page 23, Line 22: “included” -> “include”
Page 24, Line 5: “simulator is ran” -> “simulator is run”
Page 25, Line 17: \citet{MDRA} -> citep{MDRA}
Page 26, Eq 19: u_{gas,a} has not been introduced
Page 27, Line 14: “… for GeoCarb…” -> “…for our GeoCarb simulation…”
Page 28, Line 1: “CO” -> “XCO”
Page 28, Line 9: “certain amount soundings” -> “certain amount of soundings”
Page 28, Line 10: “…due to much…” -> “…due to too much…”?
Figures 5-7, 10-18: there is an issue with the rendering of text in all of these (labels, etc.). Please update.
Page 31, Line 18: “do to” -> “due to”
Page 31, Lines 21-22: “where” -> “were”
Page 31, Line 26: XCO2 subscript contains zero, not O.
Page 31, Line 27: “…operational OCO-2 XCO2 retrieval…” -> “…operational OCO-2 XCO2 bias correction…”
Page 32, Figure 8: dP-from-L2 plot: missing negative sign on axis label
Page 32, Figure 8: CO2-grad-delta plot: missing negative sign on axis label
Page 33, Table 7: add units to mu, sigma, RMS
Table 8: explain abbreviation “DU=wat+SS” etc in caption.
Table 8, Table 9: change spelling of XCO, XCO2, XCH4 to X$_CO$, X$_CO_2$, X$_CH_4$ to be consistent with the rest of the manuscript.
Figure 9, caption: “Show are” -> “Shown are”
Figure 9, caption: “and the with noise results” -> reword
Page 35, Line 6: “…retrievals of…are driven by…” -> “…errors of…are driven by…”
Page 35, Line 16: “sensitive to the overall…calibration” -> “sensitive to an offset in the overall…calibration”
Page 36, Lines 6-7: Rephrase: there is very little CO2 in bands 1 and 4.
Page 39, Line 8: “ppm. 3.6…” remove “.”
Page 42, Line 18: “instrument affects” -> “instrument effects”
Page 42, Lines 18, 20: “affects” -> “effects”
References:
Baker et al. 2010: one comma too many
O’Brien et al. 2009: add url or doi.
GeoCarb MDRA: add url or doi.
Citation: https://doi.org/10.5194/amt-2023-17-RC2
Gregory R. McGarragh et al.
Gregory R. McGarragh et al.
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