the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jan 2026
Novel method to locate and quantify point-source methane emissions using time series of ground-based column observations
Abstract. Identifying and quantifying local methane emitters remains a major challenge for atmospheric monitoring. We present a novel top-down method to estimate both the upwind location and emission strength of an unknown atmospheric source from a time series of concentration observations. The approach employs backward trajectories from a Lagrangian Particle Dispersion Model (LPDM) to derive a characteristic transfer function for each potential source region. The transfer function that best reproduces the observed enhancement identifies the most likely source location. In a second step, the emission strength is inferred from the particle ensemble and its corresponding surface footprint.
The method was developed and tested using data from a six-week measurement campaign in the San Francisco Bay Area, where six EM27/SUN near-infrared Fourier transform spectrometers were operated as part of a collaborative effort to quantify greenhouse gas emissions. At the UC Berkeley site, one instrument recorded a strictly periodic methane enhancement of approximately 10 ppb occurring every 12 minutes. Since co-emitted species showed no correlation with this pattern, the signal was attributed to a single, point-like, puff-emitting methane source.
Favourable meteorological conditions enabled the analysis of several enhancement peaks. The retrieved average emission strength during the emission episodes was 0.8–78 g CH4 s-1 (equivalent to 2.1–190 metric tons yr-1). Although the exact source could not be identified in the field, the emission characteristics are consistent with periodic natural-gas venting from a heating system with an installed power output of approximately 500–1000 kW installed power. The study demonstrates the potential of this approach for detecting and characterising local methane emitters from ground-based remote-sensing observations.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-204', Anonymous Referee #1, 11 Mar 2026
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RC2: 'Comment on egusphere-2026-204', Anonymous Referee #2, 06 May 2026
Summary
This manuscript presents a top-down method to estimate the location and emission strength of a point-like methane source from time series of ground-based total column observations. The approach combines a Lagrangian Particle Dispersion Model (STILT/HYSPLIT) with linear time-invariant (LTI) system theory to construct source-specific transport kernels, and applies these in a two-step inversion: first to constrain the maximum upwind source distance, then to retrieve emission strength. The method is demonstrated on a case study from the San Francisco Bay Area, where a EM27/SUN spectrometer detected striking periodic (~12-minute) methane enhancements at UC Berkeley.
The topic is relevant, the observational signal is intriguing, and the system-theoretic framing of atmospheric transport is a genuinely interesting conceptual contribution. However, the manuscript has several significant weaknesses in terms of methodological rigor, uncertainty characterisation, and clarity of presentation that I believe need to be addressed before publication.
Major comments
- Novelty claims need more careful positioning
The authors frame LTI-based interpretation of backward trajectories as a novel contribution, and it is certainly an elegant framing. However, the manuscript does not engage sufficiently with closely related prior work. For example, the use of the temporal structure of Lagrangian footprints to infer source distances has conceptual overlap with cross-correlation-based source attribution methods. The authors briefly cite Patel et al. (2025) as using the temporal evolution of methane plumes but dismiss this as using "the temporal development of the plume field itself" rather than back-trajectories. This distinction deserves a more detailed and quantitative comparison. What does the LTI framing enable that existing methods do not? A more rigorous discussion of the novelty relative to the literature is needed.
- The two-step method: step 1 is underspecified
The core of Step 1 is to find the upwind distance at which the standard deviation of fit residuals (ρ_s) is minimised. However, the physical rationale for why this minimum corresponds specifically to the maximum upwind source distance (r_max), rather than the most likely distance, is not clearly justified. If the true source is at distance d, what happens to ρ_s for segments at d/2 or 2d? The authors show one example (Figure 4) but no systematic sensitivity analysis. Is the residual minimum always a clear, unambiguous feature, or can it be broad, shallow, or multi-modal? This is critical for the claimed general applicability of the method.
- Single-case study limits generalisability
The entire demonstration rests on a single morning (3 November 2016) with a remarkably clean, periodic, high-amplitude signal. While this makes for an ideal test case, it also makes it difficult to assess the method's robustness under more typical conditions — e.g., irregular or lower-amplitude enhancements, variable wind conditions, or multiple overlapping sources. The authors acknowledge this limitation briefly in the conclusions, but the scope of the claims (e.g., "The study demonstrates the potential of this approach for detecting and characterising local methane emitters") goes considerably beyond what a single case study can support. At minimum, the authors should discuss more explicitly under what signal-to-noise conditions the method is expected to succeed or fail, and what minimum peak amplitude or recurrence regularity is required.
- Uncertainty analysis is insufficient
Section 2.7 is the weakest part of the paper. The stated overall uncertainty of 30–70% is dominated, as the authors acknowledge, by wind speed errors. However:
- The propagation formula (Eq. 9) assumes a simple linear relationship between wind speed error and emission uncertainty. This is only valid under idealised conditions (uniform wind, simple dilution). The derivation and its assumptions should be stated explicitly and their validity in complex terrain assessed.
- The ensemble variability used as the "method-intrinsic" uncertainty conflates spatial representativeness of the upwind segments with actual inversion uncertainty. These should be separated.
- The wind speed correction applied (using observed LBNL1 wind speed instead of HRRR output) is described briefly in Appendix G but is a significant post-hoc correction, with the HRRR underestimating observed wind speeds by up to ~200% at some times (Figure G4). This correction is large enough that it should be discussed in the main text, not relegated to an appendix. How sensitive are the final emission estimates to this correction?
- Observation noise is dismissed as a "secondary effect," but no quantitative estimate is provided.
- Source identification is speculative
The authors conclude that the most plausible source is a natural gas boiler at Foothill Dining or Student Housing, based on Google Street View imagery. This identification is explicitly unconfirmed, as the authors note that "attempts to contact the facility management... were unsuccessful." While speculative attribution is understandable in a methodological paper, the emissions reported in Table 1 (52–67 t CH₄ yr⁻¹) are presented with a degree of precision that is not warranted given the unresolved source identity, the wide range of individual event estimates (min/max spanning two orders of magnitude), and the substantial wind-correction uncertainties. The annual upscaling in particular, which assumes the event pattern persists year-round, is not discussed critically.
Minor comments
- The manuscript contains several grammatical and typographical errors that should be corrected (e.g., "ovserved" in Figure 3 caption; "Te ensemble range" in Figure 5 caption; "hords of voluntary contributors" in Acknowledgements).
- Equation (B1) uses non-standard notation (the set Y is undefined before use). Please define all symbols at first use.
- The choice to use the 10th percentile in a 60-minute rolling window as the background is reasonable, but the authors state this choice is "uncritical" without demonstrating this quantitatively. A brief sensitivity test (e.g., 5th vs. 15th percentile, 30 vs. 90 min window) would strengthen this claim.
- Figure 4: the bottom panel shows total emission on a log scale, which is appropriate given the dynamic range, but the caption does not explain why emissions increase so strongly with upwind distance. An intuitive explanation in the text (dilution of the footprint sensitivity with distance) would help readers interpret this.
- The paper mentions a "six-week measurement campaign" with six instruments but only analyses data from one instrument on one morning. Some context on why data from other instruments and other days were not used (or usable) for this analysis would be helpful.
- Appendix A (LTI systems) is well-written and appropriately concise, but the claim that time-invariance is "implicitly assumed with the use of backward trajectories" is too brief. This is a non-trivial assumption in a non-stationary atmosphere; its implications for the method should be discussed more carefully.
- Code and data are not yet publicly available. For a methods paper, this significantly limits reproducibility. The authors should provide at minimum a timeline for public release.
Recommendation
Major revisions required. The paper addresses a relevant and interesting problem, and the LTI framing of Lagrangian transport is an original perspective worth publishing. However, the method validation rests on a single idealised case, the uncertainty analysis needs substantial strengthening, and several methodological choices require better justification. I encourage the authors to revise accordingly and resubmit.
Citation: https://doi.org/10.5194/egusphere-2026-204-RC2
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General Comments:
This manuscript by Klappenbach et al. uses a top-down approach to quantify the emission strength and upwind location of an unknown atmospheric source from a time series of concentration observations. They use a Lagrangian Particle Dispersion Model. Data from six Fourier transform spectrometers (EM27/SUN) from a six-week long campaign in the San Francisco Bay Area were used to develop and test the method. An EM27/SUN at UC Berkeley recorded periodic methane enhancements, attributing the signal to a single point-like, puff-emitting methane source. The study shows how this approach can be used to detect and characterize local methane emitters from ground-based remote-sensing observations.
This manuscript is well-written and adequately supported by figures. This manuscript may be relevant for publication after addressing the changes suggested below. The analysis uses an older PROFFAST subversion (v2.2). The authors must consider updating the analysis to the latest PROFFAST v2.4.1 subversion.
The data availability is currently listed as “can be provided upon request.” This may not guarantee a timeline for when the data can be made available to the reader who requested them. For complete transparency, I would strongly recommend the data used in the analysis to be made publicly available either in its original format or as a subset that can be used to remake the figures or reproduce the analysis by the reader, should they choose to do so.
Specific Comments:
L19: Please capitalize G,W, and P, if capitalizing in the acronym.
L24: Please add citations/references.
L30: Here and in other places, is the order of the citations alphabetical or chronological?
L30: Please change “by” to “using”.
L33: Just citing the previously published works without listing them as "e.g." should also be fine.
L63: Please expand on this explanation further.
L75: Isn't PROFFAST v2.4.1 the latest version? Can the analysis be updated to this PROFFAST version?
L89: Please change “encapsulates” to “represents”.
Figure 2: Can a different color palette be used? It appears that different altitudes are being represented by the same/very similar colors (e.g., brown).
L118: Please remove “,” after “thickness” and put “we use 10 m” within parentheses.
Figure 3: Can a color besides green, such as red or pink be used here? The green and black lines appear to blend into each other.
L168: “This uncertainty can significantly increase….” - Please explain how.
L200: “Nevertheless, uncertainties remain substantial, mainly due to wind-field errors and observational noise. “ - Please be quantitative.
L205: “… a high uncertainty” – Of what value?
L207: “…method tends to overestimate” – By how much?
Figure 6: Could a different color palette be used with the circle edge colors being something bright? The current color scheme blends into the background image. Please add a colormap. Please change the color of the purple and white site markers to something bright to distinguish from the background and marker colors.
L215: Please ensure that the codes are available at the time of publication.
Data Availability: Please ensure that the data are publicly available so the reader can recreate the figures, if needed. The authors must consider either sharing the data in their original format, or a subset that can be used by the reader to recreate the figures, with the data being publicly available.
L228: Please remove “is”. It occurs twice.
L229: Please change to “trajectories discussed in Lin et al. (2003).”
L244: Given “an” observed …
L245: Please put” rmax” within parentheses.
Please move the Author contributions, Competing interests, and Acknowledgements after Figure G6. These seem to appear between different figures. The Acknowledgements seem to have been split in the footers of pages 20 and 21. Please correct this.