the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Aug 2024
Urban Area Observing System (UAOS) Simulation Experiment Using DQ-1 Total Column Concentration Observations
Abstract. Satellite observations of the total column dry-air CO2 (XCO2) have been proven to support the monitoring and constraining of fossil fuel CO2 (ffCO2) emissions at the urban scale. We utilized the XCO2 retrieval data from China’s first laser carbon satellite dedicated to comprehensive atmospheric environmental monitoring, DQ-1, in conjunction with a high-resolution transport model and a Bayesian inversion system, to establish a system for quantifying and detecting CO2 emissions in urban areas. Additionally, we quantified the impact of uncertainties from satellite measurements, transport models, and biospheric fluxes on emission inversions. To address uncertainties from the transport model, we introduced random wind direction and speed errors to the ffCO2 plumes and conducted 104 simulations to obtain the error distribution. In our pseudo-data experiments, ODIAC overestimated fossil fuel emissions for Beijing and Riyadh, while underestimating emissions for Cairo. Specifically, we simulated Beijing and leveraged DQ-1’s active remote sensing capabilities, utilizing its rapid day-night revisit ability. We assessed the impact of daily biospheric fluxes on ffXCO2 enhancements and further analyzed the diurnal variations of biospheric flux impacts on local XCO2 enhancements using three-hourly average NEE data. The results indicate that a significant proportion of local XCO2 enhancements are notably influenced by biospheric CO2 variations, potentially leading to substantial biases in ffCO2 emission estimates. Moreover, considering biospheric flux variations separately under day and night conditions can improve simulation accuracy by 20–70 %. With appropriate representations of uncertainty components and a sufficient number of satellite tracks, our constructed system can be used to quantify and constrain urban ffCO2 emissions effectively.
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RC1: 'Comment on egusphere-2024-2495', Anonymous Referee #1, 06 Sep 2024
Dear Editor,
I have reviewed the manuscript
Urban Area Observing System (UAOS) Simulation Experiment Using DQ-1 Total Column Concentration Observations,
by Jinchun Yi et al., MS No.: egusphere-2024-2495
General comments:
The manuscript uses innovative active remote sensing CO2 data from the actual Chinese DQ-1 lidar satellite mission and shows the potential of the XCO2 IPDA lidar onboard DQ-1 to assess anthropogenic CO2 fluxes from megacities. The WRF-STILT model is used to assess atmospheric transport, and the ODIAC inventory provides emission estimates which are scaled to the observations using a regional inverse modelling approach. The authors additionally present a case study attempting at separating natural and anthropogenic CO2 emissions around Beijing, and investigate uncertainties due to measurement (XCO2) and model errors (wind speed and direction).
The data treatment and modelling approach is appropriate, but I am missing more details on how the background CO2 level is determined in the lidar measurements, which, in my experience, is a crucial issue. All major points are well presented, yet some statements, particularly those concerning the natural emissions, are based on the examination of very few cases, and thus need to be re-formulated more cautiously. It would be helpful to include more megacity overpasses to consolidate the statements. Some figures have to be improved. The basic approach, the selection of two cities and the design of several figures is adopted from Ye et al (JGR-A 2020), so they should be more amply cited. The manuscript covers an important topic addressed with novel instrumentation and is a good match to ACP (or AMT). I therefore recommend accepting the manuscript, but only after my recommendations and comments have been addressed.
Mandatory changes:
- Section 2.3.3 Background XCO2: own experience tells me that determining the background XCO2 level is the most critical part of data treatment. Please be more precise in the description of your DWT approach. Use the example of Fig 6b where I find the background gradient so strong that its determination must be particularly challenging. Add one or more plots to illustrate how the DWT works. Did you test other thresholds (line 247)? Why did you select mean + 0.5 sigma(XCO2) as threshold?
- Figure 3: It is unclear where the CO2 maxima in panels a and b come from, given that panels c and d show complex wind situations. Please add the column averaged footprint figures (like fig 2c) to clarify this. Could the maximum at 24.2 N in panel a come from another source in the southeast? The easterly winds suggest this. Please explain. Panels c and d are too small to see the colored DQ-1 XCO2 data, only the orbits are visible. Explain in the caption the blue triangles.
- Figure 4: Please add the column averaged footprint figures (like fig 2c) to clarify the complex wind situations. The wind arrows are to small. All color bars seem wrong: the enhancements in the left line plots are much higher than in the right color plots.
- Figure 5: The DQ-1 orbit is not visible. The satellite image is too dark. Please mark the city center, the city limits and the TCCON site(s). The figure does not show the wind fields as stated in line 482.
- Section 3.3 Estimating Biosphere Fluxes: your statements concerning the natural emissions are based on the examination of very few cases, and thus need to be re-formulated more cautiously. For example, in line 582, only in figure 9d do the simulated enhancements align more closely with the observations, not in the other panels a, b, c. And in line 585, only figure 2c shows that the CASA and ODIAC enhancements differ significantly. Please re-formulate accordingly.
- References: in about half of all references the journal name is missing and the last author is mis-spelled. Eldering 2017, Han 2017, Miller 2014, and Wang 2014 are listed twice. In the manuscript, all citations should list only the first author and the year of publication.
Recommended minor changes:
line 23: The results of a case study indicate...
l 47: budget of the three fluxes: what do you mean? be more precise
l 49: ...emissions are located.
l 54 greenhouse gas measurements
l 85 which is onboard
l 92 a predetermined conclusion
l 99 used this tool
l 112 fine-scale trace gas transport
l 139 mention the LTAN (local time of ascending node) of DQ-1 to inform on the day/night capacity
l 158 integrated weighting function
l 189 Atmospheric Model Setting
l 228 of the ACDL product.
l 277 described in equations 1 and 2.
l 287 the number of dry-air molecules per unit volume
l 290 change the XCO2(p) term in the integral on the right side of eq 3 into CO2(p)
figure 2: the legends are too small
l 330 and 394: a LEO orbit has a velocity of ~7 km/s, so either you averaged over 7 km, or over 0.5 sec
l 387 nighttime observations can also be affected by aerosol and clouds, so explain better what you want to state here
l 399 Here, sigma represents the random error...
l 412 I guess the background XCO2 level is determined by the lidar? Please be more precise.
l 485 Figures 6e-h ...
l 648 show these averages in table 1
l 663 45%
l 744 June 2022 to April 2023
Citation: https://doi.org/10.5194/egusphere-2024-2495-RC1 -
AC1: 'Reply on RC1', Jinchun Yi, 26 Sep 2024
We thank Anonymous Referee #1 for his detailed feedback and useful comments on our paper during his busy schedule. We have elaborated on the process of running DWT on the computation of background lines with latitudinal gradients, and implemented useful suggestions on plots and tables. Please find our detailed responses to all comments and the steps we have taken in the uploaded pdf file.
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RC2: 'Comment on egusphere-2024-2495', Anonymous Referee #2, 27 Jun 2025
This is an important and interesting paper from Yi and co-authors that uses the DQ-1 satellite product to estimate CO2 fluxes from a number of urban areas. The paper is appropriate for publication in ACP with a few changes (detailed below).
There is no data availability section. This is a requirement for publication in ACP as far as I'm aware. The DQ-1 tracks used here should be archived alongside the XCO2 simulated. Please provide links to the OCO-2 and TCCON data used in the analysis, ideally with a DOI. Providing a link to the OCO-2 and TCCON data is important for the continued funding of these projects. What MODIS data was used to scale the NEE products? Please provide a link. As there is no data available, I cannot assess the XCO2 data from DQ-1. So all comments are focused on the method of an analysis, with the assumption that the data underlying it is of sufficient quality to conduct the analysis.
Major Comments:
I am a little concerned about the calculation of the background (section 2.3.3).
I'm not convinced that DWT is the correct approach to determine the background. Could you test other methods to determine the uncertainty? Maybe some of the OCO-2 approaches and see how much it impacts your result? Do you have enough tracks to ensure the DWT approach is valid?Atmospheric mixing will magnify the impact of nighttime fluxes on the atmospheric concentration (ecosystem respiration at shallow mixed layer heights) and reduce the impact of daytime flux (photosynthesis at taller mixed layer heights). Eqn 5 must be calculated using the matching hours of the column integrated footprint and the NEE (around Line 309). If the authors think that is not necessary, then they need to include a section showing why that would be the case (it might not be necessary if the biogenic fluxes are too small to matter).
I commend the authors for investigating the horizontal transport of the footprints (around Line 338). But the atmospheric concentrations calculated from footprints (convolved with inventories) are highly sensitive to the height that the emissions are mixed up to (known as mixed ayer height (MLH), which is a variable in the STILT configuration setup. This uncertainty is much larger at night as the top of the nocturnal boundary layer begins to reduce in altitude. The authors might want to quantify how sensitive the calculated footprints (and hence fluxes) are to that change in MLH at night. There is a similar magnitude of changes in the MLH between winter and summer days.
Shekar et al 2020's study of the Nile River Delta (slightly larger domain but includes the area here) show large NEE from agriculture in summers north of Cairo. They find that ODIAC (and EDGAR) are vastly underestimating FF CO2. Why do you think they get a different result? Should you include the NEE for Cairo too?
They also point out that "Burri et al (2009) measured CO2 fluxes using the eddy covariance method at the University of Cairo and showed that the CO2 fluxes have a significant diurnal and weekly variation, with peak CO2 flux occurring between 14:00 and 16:00 and minimum flux on Friday (rest day due to Muslim prayer day on Friday)". Do you know what day of the week the DQ-1 Observations were made in each of the cities?
Shekhar et al 2020 Environ. Res. Lett. 15 095010 DOI 10.1088/1748-9326/ab9cfe
Minor comments:
Line 10: You need to define CO2 the first time it's used: total column dry-air carbon dioxide (XCO2). Make sure all CO2 is subscript.
Make sure to sort out the citations throughout the manuscript. And each paragraph should have an indent. But I assume copyediting will catch all that.
line 18: define ODIAC. Or you could just say that the inventory overestimated…
Line 37: "the booming economics" is not correct. I would suggest something like "their rapidly growing/changing economies".
line 47: the "three" fluxes here was confusing. I think your response to Review 1 was sufficient.
line 62: "map the gross primary production" doesn't make sense to me. I think you mean something like "map the net CO2 uptake by the biosphere". If that's the case, I would recommend changing that wording.
Line 68: Define LIDAR the first time it's used. "are ambitious" is not used correctly here. You could say "ambitiously planned" or something like that.
Line 72: Define CO2-IPDA.
Line 77: You could start a new paragraph here and the text would flow better.
Line 84: Define ACDL.
Line 87: "figure out" is not formal enough. Use something like determine, quantify, etc
Line 91: It feels like this paragraph should come before the LIDAR discussion.
Line 99: "several scientists utilized this effect tool..." should be "several studies used this tool..."
line 115: provided should be calculated or simulated
Line 157: It would be easier to interpret the results presented later if all the relevant info about the DQ-1 configuration were together. A quick summary of the ground sampling configuration (I believe it's a 70m diameter circle sampled every 350m), frequency of overpass (how often could we image these cities in an ideal scenario?), is the track sampling from north to south or south to north? The day/night local overpass time, etc. All the info that is spread across the manuscript but could be summarized so the reader can appreciate the strength of the data.
line 165: Define WF.
Line 212: what configuration of STILT was used? Did you use the default configuration from STILT v1 or v2? I can't remember what X-STILT uses but it should be mentioned. What is the ground spatial resolution of the gridded STILT footprints? Are you using the hourly column integrated footprints or a summed 24 hour column integrated footprint (which will be a problem for NEE, see above).
line 219: The Hefner and Gilfillan citations are both incomplete. Is this from a journal? What is the DOI?
line 250: I would change "biological flux" to Biogenic carbon flux. I assume you're not including human respiration in your estimates of CO2 flux for these cities so Biological flux is not accurate. I think biogenic is a more appropriate word here.
What were the meteorological conditions for each city during the DQ-1 overpass? Was the vegetation active all year round (as might be in the tropics) or was the vegetation dormant in the Beijing winter?
What is the spatial resolution of the NEE? GFED4 has been released for years. Is there a reason you are using GFED3?
Line 263: what is the spatial resolution of the MODIS Green vegetation product you used? Is there a link to this product? (Include it in the data availability section too). Did you use year specific products or a compilation of many years?
Line 275: include the wavelengths to clarify the on and off-wavelengths
Line 282: TOA not top.
Line 291: I found the title of section 2.4.1 really confusing. I would suggest that the authors separate out the LIDAR observations (very important on their own) from the combination of STILT footprints with the inventories to create the simulated XCO2.
Line 324. The terminology is difficult to follow in this section.
The observed enhancement above background dXCO2, has been labeled as ff only. But there is not enough information available to conclude that all the emissions are fossil fuel only (biogenic, human respiration, etc). Instead of using ffXCO2_p, you could use dXCO2_obs. It would be less confusing for the reader.
Using p and a for observed and simulated is also confusing to follow. You could use obs and sim as subscripts instead and it would be easier to follow.
line 337: Start a new paragraph for the transport model.
Line 366: The satellites don't measure fluxes, they are calculated from the concentrations. So the wording here should be "such as those created using high-res..."
Line 391: What about NEE? Should that have been included too?
Line 409: which months? Is there a table of these results some where?
Line 416: The variable labelled "ffXCO2 observed enhancement" is actually the observed XCO2 enhancement with the assumption that ALL of the CO2 is from fossil fuel sources. I disagree with that assumption. While most of the CO2 will be from FF, there will also be CO2 from human respiration and biogenic fluxes could be non-zero in these cities. There is better use of the terminology in the discussion that could be used here.
Line 428: Are the wind speed shown in Fig 3 the instantaneous winds at the time of the overpass or the mean winds from WRF?
Line 432: Please specify the time zone. I assume this is 11am UTC? I would also ask for the local time alongside the UTC. It will make it easier for the reader to follow when it comes to interpreting the results.
Line 439: "Better" seems generic. Could you be more specific here?
Line 443: I assume ffXCO2 is observed? I would specify that here.
Figure 4: Most of the text on the figures is too small to see but Fig 4 is really difficult. I would recommend splitting it over two lines and make everything a little bigger. Fig 6 is easier to see.
This is more of a suggestion, than a requirement, but something I have found useful is to show figures with latitude on the Y axis, and the other variable on the X axis. So it would mean that figures with XCO2 vs Latitude are rotated to match the maps. I know that's not the normal way to do it, but it would make it easier for the reader to follow the flight track of the satellite and match the XCO2 vs Latitude to the maps.
I would also suggest indicating the outline of the city in shading in the background on these graphs if it's not there.
Line 452: Instead of Compared to, I think you mean In contrast to.
Line 458: similar in space? Maybe clarify how they are similar.
Line 459: overlooked -> did not include
Line 460: Were there any problems with the sand/dust/aerosols? Given the paper has focused on cities with high aerosol loadings, it might be worth saying more about how that is not a problem for the lidar vs other CO2 satellites.
Fig 5: I like this for Beijing. Could you do the same for the other cities? It would provide context for the readers that seems to be missing.
Line 469: Should this paragraph be in the methods?
Line 485: Fig 5 only has a,b. Do you mean Fig 6?
Line 505: But do DQ-1 and OCO-2 show the same spatial trends? Would be worth including that point here.
Line 544: Agree. Hence needing hourly convolutions of NEE and footprints.
Line 559: "Forward eight-hour". I would phrase it as the previous 8 hours before the overflight.
Line 564: Start a new paragraph.
Line 566: Shouldn't you use the same method as previously? How is this different?
Line 590: absorption phenomenon? I think you mean CO2 uptake or depletion. I would not use absorption to indicate a carbon flux when you were already talking about it as part of the retrieval process. Same on line 593.
Figure 9: Why is panel (b) not over the same latitude range? I also really like the terminology used in Figure 9 and the description. This is the kind of terminology that should be used in the methods section for the description of the STILT footprints x inventory discussion.
and Line 598: While the CASA NEE change is small, it seems that CASA is predicting uptake of CO2 by vegetation in winter (a) - Does that make sense? ODIAC predicts an increase in CO2 which makes more sense for a winter-time respiration signal (there is little diel variability in ecosystem respiration so you would expect less NEE diel variability in winter). But I'm surprised that the ODIAC enhancement is so large (>0.5 ppm additional XCO2?)
Table 1: What is the last column?
Line 619: "The table below" should be Table 1.
Line 623: "world-class" should probably be "well characterized".
Line 624: Can you clarify if the scaling factor means that the inventories are under or over estimating?
Line 636: That's an interesting point. Do you know why there is a larger transport error in one city vs the other? Would you expect to see a diel variability of CO2 emissions (there is usually a rush hour signal at least) and how would that impact the uncertainties?
Line 649: Can you include the actual estimated fluxes alongside the scaling factors?
Line 668: The "transport" error will be dominated by the higher mixed layer height. The MLH should be included in this discussion.
Line 678: The MLH will be higher in summer days and that may reduce the uncertainties for the footprints.
Line 699: All of these instruments (GeoCARB, TCCON, MicroCARB, etc) are all limited to sunlight times so you can't do day/night inversions. Only DQ-1 can do this type of study.
Line 709: VPRM is a parameterized vegetation model. You might want to cite some of the urban configurations such as:
Gourdji, S. M., Karion, A., Lopez-Coto,I., Ghosh, S., Mueller, K. L., Zhou, Y.,et al. (2022). A modified Vegetation Photosynthesis and Respiration Model (VPRM) for the eastern USA and Canada, evaluated with comparison to atmospheric observations and other biospheric models. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006290.https://doi.org/10.1029/2021JG006290
Winbourne, J. B., Smith, I. A., Stoynova,H., Kohler, C., Gately, C. K., Logan, B.A., et al. (2022). Quantification of urban forest and grassland carbon fluxes using field measurements and a satellite-based model in Washington DC/Baltimore area. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006568.https://doi.org/10.1029/2021JG006568
Wei, D., Reinmann, A. B., Schiferl, L. D., and Commane, R.: High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City, Environmental Research Letters, https://doi.org/10.1088/1748-9326/aca68f, 2022.
Line 749: Could you calculate the CO2 fluxes resulting from these scaling factors?Citation: https://doi.org/10.5194/egusphere-2024-2495-RC2 -
AC2: 'Reply on RC2', Jinchun Yi, 23 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2495/egusphere-2024-2495-AC2-supplement.pdf
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AC2: 'Reply on RC2', Jinchun Yi, 23 Jul 2025
Status: closed
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RC1: 'Comment on egusphere-2024-2495', Anonymous Referee #1, 06 Sep 2024
Dear Editor,
I have reviewed the manuscript
Urban Area Observing System (UAOS) Simulation Experiment Using DQ-1 Total Column Concentration Observations,
by Jinchun Yi et al., MS No.: egusphere-2024-2495
General comments:
The manuscript uses innovative active remote sensing CO2 data from the actual Chinese DQ-1 lidar satellite mission and shows the potential of the XCO2 IPDA lidar onboard DQ-1 to assess anthropogenic CO2 fluxes from megacities. The WRF-STILT model is used to assess atmospheric transport, and the ODIAC inventory provides emission estimates which are scaled to the observations using a regional inverse modelling approach. The authors additionally present a case study attempting at separating natural and anthropogenic CO2 emissions around Beijing, and investigate uncertainties due to measurement (XCO2) and model errors (wind speed and direction).
The data treatment and modelling approach is appropriate, but I am missing more details on how the background CO2 level is determined in the lidar measurements, which, in my experience, is a crucial issue. All major points are well presented, yet some statements, particularly those concerning the natural emissions, are based on the examination of very few cases, and thus need to be re-formulated more cautiously. It would be helpful to include more megacity overpasses to consolidate the statements. Some figures have to be improved. The basic approach, the selection of two cities and the design of several figures is adopted from Ye et al (JGR-A 2020), so they should be more amply cited. The manuscript covers an important topic addressed with novel instrumentation and is a good match to ACP (or AMT). I therefore recommend accepting the manuscript, but only after my recommendations and comments have been addressed.
Mandatory changes:
- Section 2.3.3 Background XCO2: own experience tells me that determining the background XCO2 level is the most critical part of data treatment. Please be more precise in the description of your DWT approach. Use the example of Fig 6b where I find the background gradient so strong that its determination must be particularly challenging. Add one or more plots to illustrate how the DWT works. Did you test other thresholds (line 247)? Why did you select mean + 0.5 sigma(XCO2) as threshold?
- Figure 3: It is unclear where the CO2 maxima in panels a and b come from, given that panels c and d show complex wind situations. Please add the column averaged footprint figures (like fig 2c) to clarify this. Could the maximum at 24.2 N in panel a come from another source in the southeast? The easterly winds suggest this. Please explain. Panels c and d are too small to see the colored DQ-1 XCO2 data, only the orbits are visible. Explain in the caption the blue triangles.
- Figure 4: Please add the column averaged footprint figures (like fig 2c) to clarify the complex wind situations. The wind arrows are to small. All color bars seem wrong: the enhancements in the left line plots are much higher than in the right color plots.
- Figure 5: The DQ-1 orbit is not visible. The satellite image is too dark. Please mark the city center, the city limits and the TCCON site(s). The figure does not show the wind fields as stated in line 482.
- Section 3.3 Estimating Biosphere Fluxes: your statements concerning the natural emissions are based on the examination of very few cases, and thus need to be re-formulated more cautiously. For example, in line 582, only in figure 9d do the simulated enhancements align more closely with the observations, not in the other panels a, b, c. And in line 585, only figure 2c shows that the CASA and ODIAC enhancements differ significantly. Please re-formulate accordingly.
- References: in about half of all references the journal name is missing and the last author is mis-spelled. Eldering 2017, Han 2017, Miller 2014, and Wang 2014 are listed twice. In the manuscript, all citations should list only the first author and the year of publication.
Recommended minor changes:
line 23: The results of a case study indicate...
l 47: budget of the three fluxes: what do you mean? be more precise
l 49: ...emissions are located.
l 54 greenhouse gas measurements
l 85 which is onboard
l 92 a predetermined conclusion
l 99 used this tool
l 112 fine-scale trace gas transport
l 139 mention the LTAN (local time of ascending node) of DQ-1 to inform on the day/night capacity
l 158 integrated weighting function
l 189 Atmospheric Model Setting
l 228 of the ACDL product.
l 277 described in equations 1 and 2.
l 287 the number of dry-air molecules per unit volume
l 290 change the XCO2(p) term in the integral on the right side of eq 3 into CO2(p)
figure 2: the legends are too small
l 330 and 394: a LEO orbit has a velocity of ~7 km/s, so either you averaged over 7 km, or over 0.5 sec
l 387 nighttime observations can also be affected by aerosol and clouds, so explain better what you want to state here
l 399 Here, sigma represents the random error...
l 412 I guess the background XCO2 level is determined by the lidar? Please be more precise.
l 485 Figures 6e-h ...
l 648 show these averages in table 1
l 663 45%
l 744 June 2022 to April 2023
Citation: https://doi.org/10.5194/egusphere-2024-2495-RC1 -
AC1: 'Reply on RC1', Jinchun Yi, 26 Sep 2024
We thank Anonymous Referee #1 for his detailed feedback and useful comments on our paper during his busy schedule. We have elaborated on the process of running DWT on the computation of background lines with latitudinal gradients, and implemented useful suggestions on plots and tables. Please find our detailed responses to all comments and the steps we have taken in the uploaded pdf file.
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RC2: 'Comment on egusphere-2024-2495', Anonymous Referee #2, 27 Jun 2025
This is an important and interesting paper from Yi and co-authors that uses the DQ-1 satellite product to estimate CO2 fluxes from a number of urban areas. The paper is appropriate for publication in ACP with a few changes (detailed below).
There is no data availability section. This is a requirement for publication in ACP as far as I'm aware. The DQ-1 tracks used here should be archived alongside the XCO2 simulated. Please provide links to the OCO-2 and TCCON data used in the analysis, ideally with a DOI. Providing a link to the OCO-2 and TCCON data is important for the continued funding of these projects. What MODIS data was used to scale the NEE products? Please provide a link. As there is no data available, I cannot assess the XCO2 data from DQ-1. So all comments are focused on the method of an analysis, with the assumption that the data underlying it is of sufficient quality to conduct the analysis.
Major Comments:
I am a little concerned about the calculation of the background (section 2.3.3).
I'm not convinced that DWT is the correct approach to determine the background. Could you test other methods to determine the uncertainty? Maybe some of the OCO-2 approaches and see how much it impacts your result? Do you have enough tracks to ensure the DWT approach is valid?Atmospheric mixing will magnify the impact of nighttime fluxes on the atmospheric concentration (ecosystem respiration at shallow mixed layer heights) and reduce the impact of daytime flux (photosynthesis at taller mixed layer heights). Eqn 5 must be calculated using the matching hours of the column integrated footprint and the NEE (around Line 309). If the authors think that is not necessary, then they need to include a section showing why that would be the case (it might not be necessary if the biogenic fluxes are too small to matter).
I commend the authors for investigating the horizontal transport of the footprints (around Line 338). But the atmospheric concentrations calculated from footprints (convolved with inventories) are highly sensitive to the height that the emissions are mixed up to (known as mixed ayer height (MLH), which is a variable in the STILT configuration setup. This uncertainty is much larger at night as the top of the nocturnal boundary layer begins to reduce in altitude. The authors might want to quantify how sensitive the calculated footprints (and hence fluxes) are to that change in MLH at night. There is a similar magnitude of changes in the MLH between winter and summer days.
Shekar et al 2020's study of the Nile River Delta (slightly larger domain but includes the area here) show large NEE from agriculture in summers north of Cairo. They find that ODIAC (and EDGAR) are vastly underestimating FF CO2. Why do you think they get a different result? Should you include the NEE for Cairo too?
They also point out that "Burri et al (2009) measured CO2 fluxes using the eddy covariance method at the University of Cairo and showed that the CO2 fluxes have a significant diurnal and weekly variation, with peak CO2 flux occurring between 14:00 and 16:00 and minimum flux on Friday (rest day due to Muslim prayer day on Friday)". Do you know what day of the week the DQ-1 Observations were made in each of the cities?
Shekhar et al 2020 Environ. Res. Lett. 15 095010 DOI 10.1088/1748-9326/ab9cfe
Minor comments:
Line 10: You need to define CO2 the first time it's used: total column dry-air carbon dioxide (XCO2). Make sure all CO2 is subscript.
Make sure to sort out the citations throughout the manuscript. And each paragraph should have an indent. But I assume copyediting will catch all that.
line 18: define ODIAC. Or you could just say that the inventory overestimated…
Line 37: "the booming economics" is not correct. I would suggest something like "their rapidly growing/changing economies".
line 47: the "three" fluxes here was confusing. I think your response to Review 1 was sufficient.
line 62: "map the gross primary production" doesn't make sense to me. I think you mean something like "map the net CO2 uptake by the biosphere". If that's the case, I would recommend changing that wording.
Line 68: Define LIDAR the first time it's used. "are ambitious" is not used correctly here. You could say "ambitiously planned" or something like that.
Line 72: Define CO2-IPDA.
Line 77: You could start a new paragraph here and the text would flow better.
Line 84: Define ACDL.
Line 87: "figure out" is not formal enough. Use something like determine, quantify, etc
Line 91: It feels like this paragraph should come before the LIDAR discussion.
Line 99: "several scientists utilized this effect tool..." should be "several studies used this tool..."
line 115: provided should be calculated or simulated
Line 157: It would be easier to interpret the results presented later if all the relevant info about the DQ-1 configuration were together. A quick summary of the ground sampling configuration (I believe it's a 70m diameter circle sampled every 350m), frequency of overpass (how often could we image these cities in an ideal scenario?), is the track sampling from north to south or south to north? The day/night local overpass time, etc. All the info that is spread across the manuscript but could be summarized so the reader can appreciate the strength of the data.
line 165: Define WF.
Line 212: what configuration of STILT was used? Did you use the default configuration from STILT v1 or v2? I can't remember what X-STILT uses but it should be mentioned. What is the ground spatial resolution of the gridded STILT footprints? Are you using the hourly column integrated footprints or a summed 24 hour column integrated footprint (which will be a problem for NEE, see above).
line 219: The Hefner and Gilfillan citations are both incomplete. Is this from a journal? What is the DOI?
line 250: I would change "biological flux" to Biogenic carbon flux. I assume you're not including human respiration in your estimates of CO2 flux for these cities so Biological flux is not accurate. I think biogenic is a more appropriate word here.
What were the meteorological conditions for each city during the DQ-1 overpass? Was the vegetation active all year round (as might be in the tropics) or was the vegetation dormant in the Beijing winter?
What is the spatial resolution of the NEE? GFED4 has been released for years. Is there a reason you are using GFED3?
Line 263: what is the spatial resolution of the MODIS Green vegetation product you used? Is there a link to this product? (Include it in the data availability section too). Did you use year specific products or a compilation of many years?
Line 275: include the wavelengths to clarify the on and off-wavelengths
Line 282: TOA not top.
Line 291: I found the title of section 2.4.1 really confusing. I would suggest that the authors separate out the LIDAR observations (very important on their own) from the combination of STILT footprints with the inventories to create the simulated XCO2.
Line 324. The terminology is difficult to follow in this section.
The observed enhancement above background dXCO2, has been labeled as ff only. But there is not enough information available to conclude that all the emissions are fossil fuel only (biogenic, human respiration, etc). Instead of using ffXCO2_p, you could use dXCO2_obs. It would be less confusing for the reader.
Using p and a for observed and simulated is also confusing to follow. You could use obs and sim as subscripts instead and it would be easier to follow.
line 337: Start a new paragraph for the transport model.
Line 366: The satellites don't measure fluxes, they are calculated from the concentrations. So the wording here should be "such as those created using high-res..."
Line 391: What about NEE? Should that have been included too?
Line 409: which months? Is there a table of these results some where?
Line 416: The variable labelled "ffXCO2 observed enhancement" is actually the observed XCO2 enhancement with the assumption that ALL of the CO2 is from fossil fuel sources. I disagree with that assumption. While most of the CO2 will be from FF, there will also be CO2 from human respiration and biogenic fluxes could be non-zero in these cities. There is better use of the terminology in the discussion that could be used here.
Line 428: Are the wind speed shown in Fig 3 the instantaneous winds at the time of the overpass or the mean winds from WRF?
Line 432: Please specify the time zone. I assume this is 11am UTC? I would also ask for the local time alongside the UTC. It will make it easier for the reader to follow when it comes to interpreting the results.
Line 439: "Better" seems generic. Could you be more specific here?
Line 443: I assume ffXCO2 is observed? I would specify that here.
Figure 4: Most of the text on the figures is too small to see but Fig 4 is really difficult. I would recommend splitting it over two lines and make everything a little bigger. Fig 6 is easier to see.
This is more of a suggestion, than a requirement, but something I have found useful is to show figures with latitude on the Y axis, and the other variable on the X axis. So it would mean that figures with XCO2 vs Latitude are rotated to match the maps. I know that's not the normal way to do it, but it would make it easier for the reader to follow the flight track of the satellite and match the XCO2 vs Latitude to the maps.
I would also suggest indicating the outline of the city in shading in the background on these graphs if it's not there.
Line 452: Instead of Compared to, I think you mean In contrast to.
Line 458: similar in space? Maybe clarify how they are similar.
Line 459: overlooked -> did not include
Line 460: Were there any problems with the sand/dust/aerosols? Given the paper has focused on cities with high aerosol loadings, it might be worth saying more about how that is not a problem for the lidar vs other CO2 satellites.
Fig 5: I like this for Beijing. Could you do the same for the other cities? It would provide context for the readers that seems to be missing.
Line 469: Should this paragraph be in the methods?
Line 485: Fig 5 only has a,b. Do you mean Fig 6?
Line 505: But do DQ-1 and OCO-2 show the same spatial trends? Would be worth including that point here.
Line 544: Agree. Hence needing hourly convolutions of NEE and footprints.
Line 559: "Forward eight-hour". I would phrase it as the previous 8 hours before the overflight.
Line 564: Start a new paragraph.
Line 566: Shouldn't you use the same method as previously? How is this different?
Line 590: absorption phenomenon? I think you mean CO2 uptake or depletion. I would not use absorption to indicate a carbon flux when you were already talking about it as part of the retrieval process. Same on line 593.
Figure 9: Why is panel (b) not over the same latitude range? I also really like the terminology used in Figure 9 and the description. This is the kind of terminology that should be used in the methods section for the description of the STILT footprints x inventory discussion.
and Line 598: While the CASA NEE change is small, it seems that CASA is predicting uptake of CO2 by vegetation in winter (a) - Does that make sense? ODIAC predicts an increase in CO2 which makes more sense for a winter-time respiration signal (there is little diel variability in ecosystem respiration so you would expect less NEE diel variability in winter). But I'm surprised that the ODIAC enhancement is so large (>0.5 ppm additional XCO2?)
Table 1: What is the last column?
Line 619: "The table below" should be Table 1.
Line 623: "world-class" should probably be "well characterized".
Line 624: Can you clarify if the scaling factor means that the inventories are under or over estimating?
Line 636: That's an interesting point. Do you know why there is a larger transport error in one city vs the other? Would you expect to see a diel variability of CO2 emissions (there is usually a rush hour signal at least) and how would that impact the uncertainties?
Line 649: Can you include the actual estimated fluxes alongside the scaling factors?
Line 668: The "transport" error will be dominated by the higher mixed layer height. The MLH should be included in this discussion.
Line 678: The MLH will be higher in summer days and that may reduce the uncertainties for the footprints.
Line 699: All of these instruments (GeoCARB, TCCON, MicroCARB, etc) are all limited to sunlight times so you can't do day/night inversions. Only DQ-1 can do this type of study.
Line 709: VPRM is a parameterized vegetation model. You might want to cite some of the urban configurations such as:
Gourdji, S. M., Karion, A., Lopez-Coto,I., Ghosh, S., Mueller, K. L., Zhou, Y.,et al. (2022). A modified Vegetation Photosynthesis and Respiration Model (VPRM) for the eastern USA and Canada, evaluated with comparison to atmospheric observations and other biospheric models. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006290.https://doi.org/10.1029/2021JG006290
Winbourne, J. B., Smith, I. A., Stoynova,H., Kohler, C., Gately, C. K., Logan, B.A., et al. (2022). Quantification of urban forest and grassland carbon fluxes using field measurements and a satellite-based model in Washington DC/Baltimore area. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006568.https://doi.org/10.1029/2021JG006568
Wei, D., Reinmann, A. B., Schiferl, L. D., and Commane, R.: High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City, Environmental Research Letters, https://doi.org/10.1088/1748-9326/aca68f, 2022.
Line 749: Could you calculate the CO2 fluxes resulting from these scaling factors?Citation: https://doi.org/10.5194/egusphere-2024-2495-RC2 -
AC2: 'Reply on RC2', Jinchun Yi, 23 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2495/egusphere-2024-2495-AC2-supplement.pdf
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AC2: 'Reply on RC2', Jinchun Yi, 23 Jul 2025
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