the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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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
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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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