Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts

Barré, Jérôme; Aben, Ilse; Agustí-Panareda, Anna; Balsamo, Gianpaolo; Bousserez, Nicolas; Dueben, Peter; Engelen, Richard; Inness, Antje; Lorente, Alba; McNorton, Joe; Peuch, Vincent-Henri; Radnoti, Gabor; Ribas, Roberto

doi:https://doi.org/10.5194/acp-21-5117-2021

Articles | Volume 21, issue 6

https://doi.org/10.5194/acp-21-5117-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-21-5117-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 6

Research article

|

01 Apr 2021

Research article |

| 01 Apr 2021

Systematic detection of local CH₄ anomalies by combining satellite measurements with high-resolution forecasts

Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, and Roberto Ribas

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Final revised paper (published on 01 Apr 2021)
Preprint (discussion started on 03 Sep 2020)

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Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review', Anonymous Referee #1, 30 Sep 2020
- AC1: 'Response to RC1', Jérôme Barré, 30 Nov 2020
RC2: 'Review of “Systematic detection of local CH4 emissions anomalies combining satellite measurements and high-resolution forecasts” by Barré et al.', Anonymous Referee #2, 08 Nov 2020
- AC2: 'Response to RC2', Jérôme Barré, 30 Nov 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Katja Gänger on behalf of the Authors (01 Dec 2020) Author's response

ED: Referee Nomination & Report Request started (01 Dec 2020) by Andreas Richter

RR by Anonymous Referee #1 (04 Dec 2020)

Suggestions for revision or reasons for rejection

General Comments

I appreciate the further analyses as well as the additional global view in Figure 9 and also understand that it is hardly possible to carry out a complete and comprehensive analysis of the capabilities and short-comings of the method within the framework of this paper. Therefore, I think that a publication in AMT would be more appropriate to present an introduction of a new interesting technical method but I do not insist on moving the article if the editor has a different opinion.

However, without a more detailed evaluation of the global capabilities some of the statements and conclusions have to be weakened (see below). I would also propose to emphasise more clearly what is actually done already in the title and/or in the abstract: local XCH4 anomalies are detected (by combining satellite measurements and forecasts), which are due either to actual emission anomalies relative to the CAMS emissions or systematic biases in the satellite data or a combination of both.

The distinction between the two cases has to be made by (subjective) interpretation and is prone to error. I do not think that the decision should be made by solely assessing the persistence of features as suggested in the manuscript. Persistent emissions can also lead to persistent features as there is not always a clear plume structure in satellite GHG data, in particular for non-point sources, when topography causes accumulation, and when using a 30-day time window (see also specific comments). How persistent (in space and time) is a pattern allowed to be to be considered real? You need additional information, e.g. to check if the features are correlated with albedo features (like you do for Figure 13), to classify patterns as retrieval biases. In principle, you also have to check the albedo features in the cases where anomalies are expected (e.g. Permian and Turkmenistan in Figures 10 and 11) to avoid expectation bias. On the other hand, emission patterns could indeed be correlated with albedo (e.g. wetlands or facilities vs. surroundings) complicating the interpretation. Please elaborate more on these issues and discuss them in a more balanced way.

For example, in case of the new example shown in Figure 14, where there is no correlation of the outlier pattern with albedo, I would be cautious to classify this feature as retrieval bias unless another good explanation for a potential retrieval bias has been found.

Specific Comments

Page 2, Lines 58-59: The sentence is a little misleading because the cited papers analyse different regions, but all include the Permian basin. Therefore, I suggest to change it to something like: "... large and extended enhancements in different US oil and gas production regions such as the Permian basin."

Page 3, Lines 86-87: What is the difference between instrument precision and random error?

Page 4, Lines 117-121: Your data assimilation technique only corrects the concentrations and not the emissions. But this is exactly the potential problem, isn't it? As a consequence, H(x) (in ppb) potentially depends on patterns observed by IASI and TANSO. Or am I getting something wrong here? Assume there is a (unknown) source, which is observed by TANSO and TROPOMI. Then the concentrations in the forecast are corrected upwards due to TANSO and the difference d to TROPOMI (which also sees an enhancement) is getting smaller because of the assimilation. The other way round, isn't it possible that a potential bias in the IASI or TANSO data, which is assimilated, causes an artificial outlier of your method although emission data bases are actually consistent with the TROPOMI measurements? Along these lines, wouldn't it be better to use a model without assimilation of satellite data as starting point if you want to assess the quality of emission data bases?

Page 6, Lines 166-168: Is the averaging kernel function as a function of pressure really discussed in the cited paper? Moreover, the paper analyses a different algorithm than the one used here. Please cite a paper describing the averaging kernels of the operational TROPOMI algorithm if possible.

Page 9, Lines 265-267: Please check if there is actually a correlation with surface albedo features (as in the case of Figure 13).

Page 10, Lines 301-303: This statement is too strong. Please write e.g. "potential retrieval error artefacts". Persistence isn't everything because plumes are not always visible in daily GHG data and may disappear when using multi-day time windows if the wind direction changes. As a consequence, it is possible that you only get an anomaly right above the source with your method (see also general comments). Please revise this section accordingly.

Page 10, Lines 305-307: I would be cautious to classify this feature as retrieval bias when there is no correlation with albedo features. Are there other potential explanations? (Other features causing biases? Could it be a real signal?)

Page 11, Lines 317-318: Please add a sentence that the distinction between over-/under-reported sources and local retrieval errors is challenging and needs correlation analyses with external data sets such as albedo.

Figures 9-14: The colours of the four categories are sometimes hard to distinguish in the maps (in particular with the updated colours in the revised version). Please consider to use different colours or to code the classes additionally in a different way (for example by different symbol shapes or hatching).

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ED: Publish subject to minor revisions (review by editor) (16 Dec 2020) by Andreas Richter

Dear Jérôme Barré,

I have now read your revised manuscript and the comments by the reviewer. As already mentioned during the quick review and now again by the reviewer, ACP is not the ideal journal for this manuscript but I think this should not be changed now.

Based on the review and my own reading of the text, I will accept the manuscript after minor revisions. The reviewer has made a number of comments and suggestions which you should take into consideration when revising your manuscript. In addition, here are a couple of comments from my side.

1) I’m not a modeller which is probably why the discussion of the monitoring suite and the departure is confusing to me:
a. What do you mean by “trajectory” – for me, that is a Lagrangian calculation of the track of an air mass but here it seems to have another meaning
b. What is the difference between what is done in the monitoring suite and what I would have naively done, namely comparing the model forecast for the time of measurement with the measurement after applying the measurement operator to the model profile?
c. What is the time step of the monitoring suite?

2) I do not understand your outlier classification:
a. What do you mean by “positive filtered observations”? Do you mean filtered observations, which are positive? If so, then the description of the green and golden classes does not match what I see in Figure 9.
b. What is the difference between first-guess and forecast? If there is none, please just use “forecast”
c. Why is the green category representative of “over-reported or under-reported plumes” as stated in line 256?
d. I have trouble seeing the benefit of separating into four instead of just two groups. It would be good to provide for each of these categories an example of an application to illustrate its usefulness.

3) As also pointed out by the reviewer, the use of this method to evaluate emission inventories is undermined by the assimilation of other satellite data. Comparison to a control run would be a cleaner way of achieving this goal.

4) I also agree with the reviewer that this type of comparison is useful to identify retrieval artefacts but it is not straight forward as it is difficult to disentangle differences coming from deficiencies in the emission inventories and modelling set-up from retrieval problems.

With best regards,

Andres Richter

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AR by Jérôme Barré on behalf of the Authors (22 Jan 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (26 Jan 2021) by Andreas Richter

Short summary

This study presents a new approach to the systematic global detection of anomalous local CH₄ concentration anomalies caused by rapid changes in anthropogenic emission levels. The approach utilises both satellite measurements and model simulations, and applies novel data analysis techniques (such as filtering and classification) to automatically detect anomalous emissions from point sources and small areas, such as oil and gas drilling sites, pipelines and facility leaks.

Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts

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Systematic detection of local CH₄ anomalies by combining satellite measurements with high-resolution forecasts