The authors provide a straightforward reanalysis of MethaneAir data that has been processed with a more point source centric algorithm. The paper is clear, well written, and very interesting to see a matched filter algorithm applied to a high spectral resolution instrument like MethaneAir. I do have a few comments, most important Comment #4, as I think these results have broad implications around the importance of point sources generally. I ask the authors to clarify and provide this context before I recommend for submission. 

1. Line 145. Can you confirm or clarify how the injection of WRF-LES concentrations was performed? You calculate transmission due to extra CH4 column concentration and then apply to MethaneAir radiance? 

2. Line 194. Why aren't matched filter retrievals suitable for estimation of total area budgets? Because the background normalization in an MF algorithm "removes" regional gradients? Ultimately in an area flux inversion, one has to create XCH4 enhancements relative to the background for assimilation. If one plotted retrieved XCH4 enhancements derived from matched filters vs. CO2 proxy, and they correlated reasonably well, I don't see why a matched filter algorithm couldn't be used. Please explain. 

3. Figure 8. Why are only 8 data points shown here, when El Abbadi et al. (2024) reports 24 controlled releases were performed for MethaneAir? Shouldn't all points be shown? Were there plumes that didn't perform well with this new algorithm applied, hence they are not shown?

4. Please include point source datasets as part of the SI

5. Related to comment #4 - I am curious about how the improvement on detection limit affects the general understanding of point vs area sources, which appears to be a central mission thrust of MethaneAir. Looking at other datasets that are available online (https://showcase.earthengine.app/view/methanesat), I count 28 point sources that were detected from RF06, while this study reports 121. Relatedly, that dataset on Google Earth Engine states that point sources make up 33,700 kg/h compared to a total flux of 91,000 kg/h (37%). How much methane total do you now quantify from point sources using this new matched filter algorithm? It appears that it would have to be higher, potentially much higher. As some bottom-up studies have leveraged MethaneAir to suggest a small contribution from emission sources above 100 kg/h (e.g., https://doi.org/10.5194/egusphere-2024-1402), it appears that the conclusions from those studies may have been an artifact of point source detection limit. Though it is out of scope for this paper to comment on those studies, it is appropriate for you to state how much total CH4 there is from the Permian scenes you processed, and how that relates to total fluxes derived from the CO2 proxy method. 

The authors provide CH4 retrieval and emission quantification methods for the MethaneAIR imaging spectrometer based on a matched filter and integrated mass enhancement method. The manuscript is well written and provides interesting results. The manuscript does not have a code and data availability section. I have some comments on the methodology that should be addressed for me to recommend the paper for publication.

Introduction

L20ff: The grouping of methane imagers based on 1600 nm and 2300 nm windows is a bit arbitrary. I would argue that the main difference between AVIRIS and MAMAP-2D instruments are the difference in spatial and spectral resolution.

L40f: Please define area sources. Is a landfill already an area source?

L47ff: Maybe already explain here why CO2-proxy retrievals are less precise than matched filters.

Method

Figure 1: Instead of arbitrary spectra for CH4, CO2 and H2O, it would be nice show spectra for typical atmospheric concentrations.

L86ff: Foote et al. (2020) introduces an albedo correction term to remove systematic errors in XCH4 plumes due to deviations between the mean spectrum and the local spectrum. The systematic errors are likely to introduce systematic errors in the emission estimates. I think it is necessary to test if the albedo correction affects the results.

L95: How do you account for varying observation angles and surface elevation during data acquisition?

L101f: The small number of samples also affect the mean vector. Did you test the effect of computing the mean vector for a larger sample on your retrieval?

L114f: Kuhlmann et al. (2024, https://doi.org/10.5194/egusphere-2024-3494) identified CH4 emissions from vent stack in Romania using AVIRIS-NG that were not visible in high-resolution images. How many plumes did you reject, because they are not linked to any infrastructure, and do you see the possibility that you miss such sources in your analysis?

L113: Do you use the plume length or the square root of the detectable plume area as length scale L?

L128ff: Effective wind speed also depends on emission height and vertical mixing. Maasakkers et al. (2022) derive their empirical equation for a landfill, which I would assume, emits near the surface, while emissions from oil and gas can be elevated from vent stacks or on top of processing facilities. How do you account for this in your method?

L139f: Please provide more information about the DI method.

L143ff: Section 2.4 does not provide enough information to judge the accuracy of the end-to-end simulator. I would assume that it does not include systematic errors in the plume, which might explain why Figure 5 shows good agreement between retrieval and input. I suggest to either remove the end-to-end simulator from the manuscript or provide more details including a more detailed analysis, which should be quite interesting.

Results

Figure 2: Please add a (rough) scale to the image.

L182ff / Figure 4: I really would like to see the difference between proxy and matched filter (as in Fig. 5). Do you find systematic differences between the methods, in particular inside the plume, what might be the reason, and how would they affect your emission estimate?

L198ff: (see my previous comment on the end-to-end simulator)

L207ff: Please provide more information how the DI method has been implemented in this study. Following Chulakadabba et al. (2023), the DI method sums over all pixels along rectangular for difference from the source location to the edge of the detectable plume (except for subtracting the background, which would be about zero for the matched filter). This isn't much different from the IME method, which sums over all pixels in the detectable plume, while excluding the background (which is close to zero).

The main difference between IME and DI method seems to be the effective wind speed. What wind speed is used for the DI method and how does compare to effective wind speed used with the IME method? Would the differences vanish if the same data source (GEOS-FP or HRRR) for the wind speed is used for both methods?

L223f: It would interesting do discuss the potential for systematic errors inside the plume here (see my previous comment).

L226f: I do not see why the detection limit should not always increase with wind speed. However, detection limit can depend on other factors such as turbulence.

L238f: What is the uncertainty of the emission rates from the controlled releases?

Figure 8: Why is the RMSE measured in ppb?

L280ff: Does the wind speed vary spatially and temporally in the campaign area during data acquisition? I expect that would affect the detection limit during the campaign.

Figure 12: Do you see a dependency of flux rates on wind speed?

L292: Does the albedo change between the campaigns?

L292f: It is unclear what do you mean with "enhanced spatial variability of ΔXCH4".

Conclusions

L312f: Please specify why a computationally efficient retrieval would improve the detection limit.

L332: I suggest adding that a major advantage is minimizing the number of false positives.