the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jan 2025
Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modelling
Abstract. Modeling the dispersion of volcanic particles following explosive eruptions is critical for aviation safety. To constrain the dispersion of volcanic plumes and assess hazards, calculations rely on accurate characterization of the eruptions source term e.g., variation of emission rate and column height with time and the prevailing wind fields. This study introduces an inverse modeling framework that integrates a Lagrangian dispersion model with lidar observations to estimate emission rates of volcanic particles released during an Etna eruption. The methodology consists of using the FLEXPART model to generate source-receptor relationships between the volcano and a lidar system that observed the ensuing volcanic plume, which then are used to derive the emission rates using the observational data. We leverage data from the ACTRIS PollyXT lidar that operates at the PANhellenic GEophysical observatory of Antikythera. The inversion algorithm utilizes lidar observations and an empirical a-priori emission profile to estimate the volcanic particle source strength, accounting for altitude and time evolution of the plume. Additionally, to study the impact wind fields have on volcanic ash forecasting, the experiment is repeated using fields that assimilate Aeolus wind lidar data. Our approach applied to the 12 March 2021 Etna eruption, accurately captures a dense aerosol layer between 8 and 12.5 km. Results show a minimal difference of the order of 2 % between the observed and the simulated ash concentrations. The presented inversion algorithm coupled with Aeolus data, optimizes both the vertical emission distribution and Etna emission rates, advancing our understanding and preparedness for volcanic events.
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RC1: 'Comment on egusphere-2024-3181', Anonymous Referee #2, 21 Jan 2025
Review of “Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modelling”
The paper by Kampouri et al. introduces an innovative inversion algorithm incorporating ground-based lidar data to estimate volcanic source emissions for a more accurate ash dispersion modelling. The methodology makes also use of wind data coming from the space wind lidar Aeolus as was first shown by Amiridis et al. (Sci. Rep., 2023). The approach answers to an important issue for the modelling of volcanic hazards for aviation when source information is not complete, thereby bridging research infrastructures and satellite missions. The results highlight the robustness of the technique when compared against ACTRIS ground-based lidar measurements as well as SEVIRI images.
The paper reads very well and is appropriate for ACP. However, I would like clarified a few things that will improve the submitted paper. Below I include for consideration specific comments and technical corrections. P1L2 means line 2 of page 1.
Specific comments
- P1L22 To which “observational data” are you referring?
- P1L27 Please mention where the plume was observed.
- §3 Is it possible to devise a diagram for the methodology? I believe that will be of great help.
- §3 Can the inversion modelling be applied to other volcanoes? Can you upscale?
- §3.1.1 What if ash/dust coexist? Will this affect the value of the conversion factor? What will be the implications in case of highly polluted areas?
- P5L133 A 3-hour average period is way too long for decision making purposes. Are you able to perform shorter averages?
- P5L140-142 Please define h.
- Table 1 I think some discussion is needed for the AERONET input parameters. Is the conversion factor calculated? As the lidar observations were made during night, at what time was the AERONET measurement was made? In case you used literature values, I would like to see the references and clearly stated in the document.
- P7L208 To what data does the “observed plume height” refer to?
- P8L211 Why the particle density is different from Table 1?
- §3.4 Can multiple/concurrent lidar observations be accommodated?
- P9L244-245 Can you confirm that the impossibility of direct retrievals refers to satellite images? If that is not the case, consider volcano radar monitoring. Radar is most sensitive to large particles and can penetrate optically thick plumes near the source.
- P9L253 Please define fine ash as it is the first time it appears in the text.
- P11L310 Why does the MER value not coincide with Table 3?
- P11L310 How was this height (10 km) estimated?
- P11L317 It is not clear to me whether you used 11.5 km in your calculations. Please clarify.
- P12L328 Are you referring to Figure 2b? That figure shows volume linear depolarization ratio, please elaborate.
- P12L329 I believe two of the references, apart from Gross et al. (2013), do not quite capture the statement you make. Miffre et al. (2011) use a 355-nm lidar different from the wavelength of the profiles shown here. Also, Pisani et al. (2012) refer to volcanic particles measured at the vent and, therefore, discusses volcanic particles with different characteristics. There are several publications to pick from.
- P13L346 Why the a-posteriori MER have this behavior? Any ideas?
- Figure 3 Why not make the time axis consistent between b) and c)?
- P15L402-403 Considering that any orbiting lidar offers a few opportunities for your approach, is this a weakness of the methodology? What is your feeling?
- Figure 5 I think the lidar map would help the interpretation.
- P16L414-417 This result seems important to be worth mentioning in the paper’s abstract.
- P17L428 The reported maximum height seems higher to my eye than that seen on Figure 5. Is it correct?
- P18L453 Is the a-posteriori “w/o” Aeolus simulation zero? Or wasn’t it possible to estimate? Please, rephrase analogously. Also, if the a-posteriori “w/o” Aeolus simulation is zero, what are the implications for the inversion scheme?
- Figure 7 I suppose that Figures 6 and 7 can be presented together. I think that “a-” is needed for priori/posteriori in the legend. Also, in the legend, state that the “A-priori “w” Aeolus” is a result of Amiridis et al. (2023). In the same style, “this study” can preplace “A-posteriori “w” Aeolus”.
- §5 Prompt data delivery is crucial for real-time warnings for aviation. For the considered inversion scheme, how long will it take to deliver data? Can it be part of an automatic procedure?
- P20L491-493 Isn’t this a finding of Amiridis et al. (2023)?
- P20L497-499 As I already mentioned, it would be nice to highlight the good performance of the inversion scheme with independent measurements as shown in figure 4. If I can take it a bit further, does the comparison of the model with the lidar profiles provide a “fair” comparison? I say this because the lidar profiles were already used in your methodology. I believe that a second lidar somewhere along the path of the plume would provide an ideal dataset to compare to.
Technical Corrections
- P1L18 Add “,” before “e.g.,”.
- P1L21 Remove “ensuing”.
- P1L23 Please give the acronym or capital letters are unnecessary.
- P1L25 Move “evolution” after the “of the plume’s” and add “the” before “wind fields”.
- P2L45 Please add “usually” before “they do not”. VAAC reports during major eruptions might contain quantitative information.
- P3L73 Please follow the Journal’s terminology on dates.
- P3L74 Remove parentheses. Replace “;” with “and”. Please take extra care when reporting literature references as I noticed several inconsistencies throughout the manuscript. In the following, I report the ones that I found.
- P3L84 Replace “sulfate” with “SO2”.
- P4L114 Remove “location”.
- P4L116 Replace “megacities” with “cities”.
- P5L118: Replace “lidar system the type of PollyXT” with “PollyXT lidar system”.
- P5L127 Replace “ang” with “and”.
- P5L129 Remove the hyperlink.
- P6L164 Remove “placed”.
- P6L170 “maintaining… medium range” What do you mean?
- P7L179 Remove “had”.
- P7L195 Remove “being”.
- P7L202 Replace “allow” with “allows”.
- P7L206 Replace “… a vertical resolution 1 km in the range extending…” with “…1 km vertical resolution…”
- P7L207 Remove parentheses and correct references accordingly.
- P7L208-209 Remove parentheses and add an opening parenthesis to “2012)”.
- P8L225 Replace “, each one being” with “of” and replace “thick” with “thickness”.
- Table 2 Remove “M.J.”
- P9L237 Remove parentheses and correct references accordingly.
- P9L242 Remove “mass eruption rate” and the parentheses from “(MER)”.
- P9L247 Replace “near the source” with “downwind”.
- P9L248 Can you rephrase the second point of the list?
- P9L254-255 Please rephrase “that can undergo long-range dispersion”.
- P10L273 Remove full stop before the opening parenthesis and replace “are further described in” with “see”).
- P10L290 Enclose Eq. (9) in parentheses.
- P11L298 Remove “mass eruption rate” and the parentheses.
- P11L299-300 Remove the parentheses from the first reference and enclose the second reference in parentheses.
- P11L307 Replace “formatted” with “formed”.
- P11L316 Remove “Simona”.
- P12L322 The cited papers already appear in Section 3.1.
- P12L325 Remove “during this period”.
- P12L326 Remove “being”.
- P12L338 Remove the second “,”.
- P13L342-343 Remove “on 12 March 2021, between 06:30 and 10:30 UTC”.
- P13L362 Remove “Simona”.
- P13L364 Remove the second “,” and “in their results”.
- P17L426 Remove “ “w” ”.
- P17L435 Replace “This enhancement” with “The better agreement”.
- P20L480 Remove “experiment”.
- P20L481-482 What do you mean by “along with… following the eruption”?
Citation: https://doi.org/10.5194/egusphere-2024-3181-RC1 -
RC2: 'Comment on egusphere-2024-3181', Anonymous Referee #1, 22 Jan 2025
General Comments: (overall quality)
The manuscript integrates the ground-based and satellite observational data with atmospheric transport models (FLEXPART) to demonstrate the impact of volcanic eruptions on different aerosol concentrations observed at different ground-based atmospheric monitoring stations. The inversion model developed in this manuscript provides an innovative solution for developing a volcanic hazard warning model for aviation when source information is incomplete, synergistically bridging ACTRIS research infrastructures, satellite missions and modeling. Although only a study is presented that targets the eruption of the Etna volcano on March 12, 2021, the results obtained demonstrate the capability of the model to estimate volcanic ash emissions from lidar and FLEXPART data.
The manuscript is quite clean from an editorial point of view, well structured and is appropriate for ACP. However, a few questions should be addressed, and I would appreciate further discussion in the manuscript.
Specific comments:
1. Page 2, Lines 60-65 (Introduction): I recommend that you add a phrase or two to improve the description of the volcanic particles radiative effects, both direct and indirect. Also, you must include some key references for that.
2. Page 3, Lines 75-79 (Introduction): Do you know of previous studies on volcanic particles (volcanic ash, sulfate) that have advanced the integration of data from remote sensing measurements and atmospheric transport modeling? I recommend that you provide an overview of what has been done before in terms of integrating observational data from ground, satellite and atmospheric transport models.
3. Page 4, Lines 95-102 (The Case of 12 March–14 March 2021 Etna Volcanic Eruption): The authors should show the relevant meteorological maps for the period with significant volcanic activity from February - March 2021 (500, 300, 200 and 100 hPa circulation) to support the conclusions on volcanic particles transport. Even if FLEXPART ingests the upper air data from ECMWF, a cross-validation with “real meteorological data” (including AEOLUS data) will make the case more convincing. Consider adding a paragraph to the results section to further explore the transport and dispersion of volcanic ash particles.
4. Page 4, Lines 113-117 (Methods and Data): The authors should include a description of the general aspects of climate and atmospheric synoptic scale circulation for the region where is located PANGEA-NOA observatory.
5. Page 6 Table 1 (Ash mass calculation using remote sensing data): I recommend that you add a sentence or two to justify the values selected in the table for lidar ratio and "volume to extinction conversion factor". Also, you must include some key references for that.
6. Page 12, Figure 2b (Results): It is unclear to me what meas the blue vertical lines. Profiles of NaN (no valid volume linear depolarization ratio)? Missing data in the time periods analyzed? Common time periods for lidar - photometer? Comment on this aspect.
7. Page 13, Lines 346-348: It is unclear to me why a-posterior MER has this behavior. Please give a short explanation for clarification.
8. Page 21, Lines 513-515 (Conclusions and discussion) Discuss how the method could be adjusted for other EARLINET/ACTRIS stations with similar lidar configurations and/or for the Earth Observation missions (current and future), detailing the data requirements and necessary adjustments.
9. What were the main technical challenges in modeling long-range transported of volcanic ash and sulfate particles?
10. How fast is the inversion scheme presented in this study and what is the confidence level of the data provided?
Technical corrections
1. Update the figures 2- 4 with larger fonts to make them more visible.
2. Figures 4-5: Change the "μgr" in the figure captions to "μg"
3. Figures 6-7: Change the "ug" in the figure captions to "μg"
4. Please specify in the text the reference point (a.s.l or a.g.l) for the altitude values in figures 3, 6, 7 and 8.
Citation: https://doi.org/10.5194/egusphere-2024-3181-RC2 -
CC1: 'Critical comment on egusphere-2024-3181', s singh, 12 Feb 2025
The work of Professor Kampouri appears promising at first glance; however, there are critical flaws in methodology and presentation that have not been fully addressed by the respected reviewers.
- The use of the inversion algorithm in this manner appears highly problematic and ad hoc. For instance, if equation (5) holds, then the shapes in equation (7) do not match. Furthermore, it is implicitly assumed in (7)-(9) that uncertainties associated with all observations are equal to 1. I do not believe this assumption to be valid. The absence of a regularization parameter in term (8) suggests that the regularization is implicitly taken as 1, but I find it difficult to believe that this is optimal. The authors do not provide any optimization scheme for the regularization parameter in equation (8) or any explanation of how epsilon has been selected. Additionally, no sensitivity study to a-priori emissions has been presented. Given these shortcomings, I find the inversion results unconvincing, raising serious questions about the subsequent findings of the study. I strongly recommend that the inversion algorithm be made available in a public repository for scrutiny.
- Estimated emissions are presented without accounting for uncertainty arising from the inversion algorithm. Considering the sparse data (only a single station), the uncertainty should be significant, making the results unreliable.
- Since the authors have SEVIRI data, they should also perform inversion for SEVIRI data and compare the results derived from lidar and SEVIRI (In fact the same team performed the same study already and presented the work at EGU: https://meetingorganizer.copernicus.org/EGU23/EGU23-13755.html). This would serve as a basic form of validation, as has also been pointed out by respected Reviewer 2. At present, there is no validation of the results. However, considering the issues raised above, the publication of this study must be considered with utmost caution.Citation: https://doi.org/10.5194/egusphere-2024-3181-CC1
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