the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Random Error and Averaging Strategies for XCO2 Observations from Spaceborne IPDA Lidar Onboard DQ-1 Satellite
Abstract. Carbon dioxide (CO2) is one of the most important anthropogenic greenhouse gases in the atmosphere, and accurate monitoring of CO2 concentration is essential for carbon flux inversion and emission reduction policies. Spaceborne integrated path differential absorption (IPDA) lidar enables day-and-night observations, high-latitude coverage, and reduced sensitivity to clouds and aerosols, showing great potential for monitoring the column-averaged dry-air mole fraction of CO2 (XCO2). However, single-shot XCO2 retrievals from spaceborne IPDA lidar generally have relatively large random errors, and their along-track averaging is affected by surface conditions and data gaps. Based on 2023 DQ-1 spaceborne IPDA lidar observations, this study calculated global single-shot XCO2 random errors using the return-signal SNRs at the online and offline wavelengths. The results show pronounced spatial heterogeneity in single-shot random errors. Larger errors occur over water bodies, permanent snow and ice, and some complex land cover types, whereas day–night differences are relatively weak. Combining MCD12C1 land cover data with random-error statistics, the global observation scenes were classified into four surface random-error categories. Allan variance analysis of continuous observation segments suggests averaging approximately 160, 200, 232, and 296 observations for the four categories, corresponding to typical along-track scales of about 54, 67, 77, and 100 km. To address data gaps and along-track discontinuities, fixed-spatial-resolution and random-error-threshold-controlled averaging strategies were compared. The fixed-spatial-resolution strategy maintains more stable spatial representativeness and is more suitable as the default averaging option, whereas the threshold-controlled strategy improves error consistency but may substantially expand the averaging window. For land–sea boundaries and other rapid surface-transition regions, a boundary mixed dynamic averaging strategy was further proposed, with surface-category fractions recommended as quality indicators. These results support random-error evaluation and averaging-strategy design for spaceborne IPDA lidar XCO2 products.
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Status: open (until 26 Sep 2026)
- RC1: 'Comment on egusphere-2026-3759', Anonymous Referee #1, 22 Aug 2026 reply
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RC2: 'Comment on egusphere-2026-3759', Anonymous Referee #2, 20 Sep 2026
reply
In “Random Error and Averaging Strategies for XCO2 Observations from Spaceborne IPDA Lidar Onboard DQ-1 Satellite,” the authors explore the variability in error in calculated XCO2 as a function of averaging window, land cover, and averaging strategy. The motivation for the work is interesting: investigating the impacts of different land surfaces and sampling conditions on XCO2 uncertainty, and exploring different averaging schemes and their trade-offs in error and spatial resolution. However, there are some major issues that would need to be addressed prior to publication.
Most importantly, the authors conduct two main error analyses: one based on idealized theoretical random noise and one based on Allan variance analysis. The idealized random noise analysis is based on signal-to-noise (SNR) values from the DQ-1 data (I presume). Throughout section 2.2.1, the authors refer to the requirement for a “sufficiently high signal-to-noise ratio,” but the definition of “sufficiently high” is not provided. Given the difference in expected return levels from different land cover types and atmospheric conditions, would the threshold for “sufficiently high” vary, given other noise sources (e.g., shot noise, amplifier noise) would also vary with different return powers? Also, the reduction of error as a function of sqrt(N) assumes that the measurements are of the same observable and are independent; however, the authors state that this is not the case in the real-world operation of these instruments (Lines 136-139). As a result, I do not know how to interpret the subsequent results, such as the values in Figures 3, 6, 11, 13, and Table 2, based on these idealized calculations.
As I understand it, the motivation for the Allan analysis is to incorporate errors and covariances that are not captured by the idealized calculations in Section 2.2.1. I agree that an Allan variance analysis should better account for the covariance and biases present in actual XCO2 data. What I do not follow is why continue to use idealized noise calculations (referred to as “mean random errors”) for the land cover and averaging strategy analyses, if there are known elements (i.e., correlated noise sources and heterogeneity between conditions in subsequent observations) that these idealized calculations do not represent.
There are also presentation issues that impact the readability and quality of the manuscript:
- A number of acronyms are used without being properly introduced. These include, but are not limited to: MCD12C1, ASCENDS, A-SCOPE, DQ-1, AVX.
- The DQ-1 data used in the analysis (e.g., Figures 2, 3, 6, etc.) are not introduced anywhere in the manuscript. What is the spaceborne platform? What is the temporal and spatial coverage of the dataset? How does this compare to other datasets and instrumentation? There is a lot of missing context.
- Throughout the text, it is often not clear what is new to this work and what is from previous studies. For example, in section 5.1, it was not clear to me which elements discussed were from this study and which were from the literature.
Citation: https://doi.org/10.5194/egusphere-2026-3759-RC2
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See attached PDF of review.