the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
New plume comparison metrics for the inversion of passive gases emissions
Pierre J. Vanderbecken
Joffrey Dumont Le Brazidec
Alban Farchi
Marc Bocquet
Yelva Roustan
Élise Potier
Grégoire Broquet
Abstract. In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of plumes from cities and industrial plants to infer the emissions of these sources can be challenging. In particular traditional atmospheric inversion techniques, relying on objective comparisons to simulations with atmospheric chemistry transport models may poorly fit the observed plume due to modelling errors rather than due to uncertainties in the emissions.
The present article discusses how these images can be properly compared to simulated concentrations to limit the weight of modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise norm may not be a good option, because it is subject to the double penalty issue inherent to its local definition. This issue is characterised by a mutation of any position shift into significant amplitude discrepancies. To circumvent this issue, we propose to either provide an upstream correction of the position misfit between the observed and simulated plumes in the usual
norm or to use a non-local metric based on the optimal transport theory, such as the Wasserstein distance.
All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with a mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position mismatch and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position errors in both analytical and realistic conditions. Furthermore, in realistic cases, we evaluate the weight of changes in the norm and the direction of the four-dimensional wind fields in our metric values. This comparison highlights the link between differences in the synoptic-scale winds direction and position error. It is found that discrepancies between two plume images due to wind direction errors in the meteorological conditions are less penalised by our new metrics with the upstream correction than without, thus avoiding the double penalty issue.
Pierre J. Vanderbecken et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on amt-2022-226', Anonymous Referee #2, 09 Nov 2022
The paper proposes a new measure for forecast performance that accounts for displacement methods. Overall, the methodology seems sound, but per comment 1 below, it is unclear if this approach is really new, or what the new contribution is. Moreover, it is unclear if the added complexity of the approach over the displacement methods (e.g., as discussed in doi: 10.1175/MWR-D-19-0256.1) adds enough value to warrant its use. Therefore, I recomment acceptance after the authors consider the comments below.
1. The method proposed is very similar to several of the field deformation approaches described by the cited Gilleland et al. paper and several since that time: e.g., see doi: 10.1016/S0022-1694(00)00343-7, doi: 10.1175/2010WAF2222365.1, doi: 10.5065/D62805JJ, doi: 10.1002/2012GL053964, and doi: 10.1175/2010WAF2222351.1 to name just a few. In particular, doi: 10.3402/tellusb.v68.31682 uses the Wasserstein distance. A thorough literature review and comparison of the differences and added utility of the present approach is necessary to put this work into the greater context of these deformation methods. As it is, it is not clear what the new contribution is over these other works.
2. How does this approach address the issues outlined in doi: 10.5065/4px3-5a05 ?
3. The authors make reference to the measure's being fairer, but it is unclear what they mean by fair in the general concept of a fair verification measure.
Citation: https://doi.org/10.5194/amt-2022-226-RC1 - AC1: 'Reply on RC1', Pierre Vanderbecken, 07 Jan 2023
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RC2: 'Comment on amt-2022-226', Anonymous Referee #1, 21 Nov 2022
The authors discuss how to compare satellite observations to simulated concentrations by limiting the weight of modelling errors due to the meteorology used to analyze the observations. The manuscript presents a lot of equations to describe the math behind the method. I’m not sure I fully understand all the details, particularly section 3. But the work generally looks sound to me. I recommend the following revision.
General comments:
- Our view is that meteorology drives the position error between the plume observed and the plume simulated by the CTM. This is the motivation of the work. However, I don’t see how solid it is. There are several contributors to the errors. I don’t see the reason why meteorology is the driver.
- I recommend the author to add a flow chart to demonstrate the method.
Specific comments:
- Line 7. It shall be analyze instead of analyse.
- The concept of pixel-wise norm has been proposed without giving any introduction. Same as double penalty issue, upstream correction, non-local matric optimal transport theory. It will be difficult for readers without strong background for this very specific field to follow. I understand that it is difficult to give the definitions for all those items in a short abstract. I would like to encourage the authors to reconsider the necessity of keeping all those items and the possibility of rephasing the paragraph in a more reader-friendly way.
- Line 40-45. Meteorology is not the only contributor to modelled bias. Such information seems missing from the text.
- Line 51. What is “position error”?
- Line 47. “the relative weight of the meteorological uncertainties within the comparison between observation and simulation cannot be easily removed through pixel-wise comparison”. I don’t quite understand the meaning of this sentence. It sounds like the aim of the comparison performed at pixel level is removing meteorological uncertainties. Please try to rephrase it. Same for “This issue is shared in other fields”. I’m not sure the sentence is clear to readers.
- Line 56. What is droplet or analogous decomposition? Please try to define before use.
- Line 58. What does “fileds” represent here?
- Line 64. What is a moving field?
Citation: https://doi.org/10.5194/amt-2022-226-RC2 - AC2: 'Reply on RC2', Pierre Vanderbecken, 07 Jan 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on amt-2022-226', Anonymous Referee #2, 09 Nov 2022
The paper proposes a new measure for forecast performance that accounts for displacement methods. Overall, the methodology seems sound, but per comment 1 below, it is unclear if this approach is really new, or what the new contribution is. Moreover, it is unclear if the added complexity of the approach over the displacement methods (e.g., as discussed in doi: 10.1175/MWR-D-19-0256.1) adds enough value to warrant its use. Therefore, I recomment acceptance after the authors consider the comments below.
1. The method proposed is very similar to several of the field deformation approaches described by the cited Gilleland et al. paper and several since that time: e.g., see doi: 10.1016/S0022-1694(00)00343-7, doi: 10.1175/2010WAF2222365.1, doi: 10.5065/D62805JJ, doi: 10.1002/2012GL053964, and doi: 10.1175/2010WAF2222351.1 to name just a few. In particular, doi: 10.3402/tellusb.v68.31682 uses the Wasserstein distance. A thorough literature review and comparison of the differences and added utility of the present approach is necessary to put this work into the greater context of these deformation methods. As it is, it is not clear what the new contribution is over these other works.
2. How does this approach address the issues outlined in doi: 10.5065/4px3-5a05 ?
3. The authors make reference to the measure's being fairer, but it is unclear what they mean by fair in the general concept of a fair verification measure.
Citation: https://doi.org/10.5194/amt-2022-226-RC1 - AC1: 'Reply on RC1', Pierre Vanderbecken, 07 Jan 2023
-
RC2: 'Comment on amt-2022-226', Anonymous Referee #1, 21 Nov 2022
The authors discuss how to compare satellite observations to simulated concentrations by limiting the weight of modelling errors due to the meteorology used to analyze the observations. The manuscript presents a lot of equations to describe the math behind the method. I’m not sure I fully understand all the details, particularly section 3. But the work generally looks sound to me. I recommend the following revision.
General comments:
- Our view is that meteorology drives the position error between the plume observed and the plume simulated by the CTM. This is the motivation of the work. However, I don’t see how solid it is. There are several contributors to the errors. I don’t see the reason why meteorology is the driver.
- I recommend the author to add a flow chart to demonstrate the method.
Specific comments:
- Line 7. It shall be analyze instead of analyse.
- The concept of pixel-wise norm has been proposed without giving any introduction. Same as double penalty issue, upstream correction, non-local matric optimal transport theory. It will be difficult for readers without strong background for this very specific field to follow. I understand that it is difficult to give the definitions for all those items in a short abstract. I would like to encourage the authors to reconsider the necessity of keeping all those items and the possibility of rephasing the paragraph in a more reader-friendly way.
- Line 40-45. Meteorology is not the only contributor to modelled bias. Such information seems missing from the text.
- Line 51. What is “position error”?
- Line 47. “the relative weight of the meteorological uncertainties within the comparison between observation and simulation cannot be easily removed through pixel-wise comparison”. I don’t quite understand the meaning of this sentence. It sounds like the aim of the comparison performed at pixel level is removing meteorological uncertainties. Please try to rephrase it. Same for “This issue is shared in other fields”. I’m not sure the sentence is clear to readers.
- Line 56. What is droplet or analogous decomposition? Please try to define before use.
- Line 58. What does “fileds” represent here?
- Line 64. What is a moving field?
Citation: https://doi.org/10.5194/amt-2022-226-RC2 - AC2: 'Reply on RC2', Pierre Vanderbecken, 07 Jan 2023
Pierre J. Vanderbecken et al.
Data sets
Passive gas plume database for metrics comparison Pierre J. Vanderbecken https://doi.org/10.5281/zenodo.6958047
Pierre J. Vanderbecken et al.
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