the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Mar 2025
Potential CO2 measurement capabilities of a transportable Near Infrared Laser Heterodyne Radiometer (LHR)
Abstract. Heterodyne detection stands as a powerful method for enhancing sensitivity limits and attaining exceptional resolution. It also offers the advantage of being transportable and the ability to make it more compact which makes it favorable for ground-based remote sensing in field campaigns. An all-fiber coupled laser heterodyne radiometer (LHR), using a wideband tunable external cavity diode laser (1520–1580 nm) as local oscillator laser was developed for CO2 measurements. Optimal absorption lines and transmission spectra of the LHR was achieved by using a balanced photodetector to suppress the relative intensity noise of the local oscillator laser. This work aims to quantify how the LHR contributes to measuring tropospheric CO2 abundances in the atmospheric column from the ground. Here, we demonstrate the LHR’s ability to measure CO2 vertical profiles through a extensive analysis of information content, channel selection, and error budget estimation. This comprehensive analysis relies on the radiative transfer model ARAHMIS, developed at the Laboratoire d’Optique Atmosphérique (LOA). Additionally, we present a comparison of the LHR with other ground-based instruments, such as the EM27/SUN and the IFS125HR from the TCCON network. Furthermore, this work supports ongoing MAGIC campaigns focused on greenhouse gas monitoring and the validation of current and future space missions such as MicroCarb and FORUM.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2025-250', Anonymous Referee #1, 08 May 2025
The manuscript describes the development and analysis of a laser heterodyne radiometer (LHR) using a widely tunable diode laser. This allows – unlike most recent LHR systems for trace gas remote sensing, which typically rely on quantum cascade lasers or similar – the measurements of full rotation-vibrations bands of molecules like CO2 and an easier access to multiple species. The authors combine this with the high spectral resolution typical for LHRs to retrieve vertical profiles of CO2 in solar occultation measurements. They follow this up with comprehensive analysis of the information content within the spectrum and of the error budget. I consider the content highly significant, for the current development of LHR systems for remote sensing in the atmosphere, and the manuscript well written. Further, I appreciate the thorough addressing of information content and channel selection - since it is time consuming it is often done in an ad-hoc trial and error way (unless maybe for large projects), so it is nice to see a practical example in recent literature. However, I think there are a few points were additional clarifications and minor improvements would allow the manuscript to be equally accessible for those members of the community with a stronger background in atmospheric remote sensing as well as for those with their main background in lab spectroscopy. So I would like to propose the following general remarks and comments for the authors to consider moving forwards and point out a few clarifications which seem necessary to me. Since I do not bring a specific expertise on the matter of the details of the retrieval, my comments will be mostly limited to technical corrections and questions of understanding in the corresponding sections.
General Remarks:
Many acronyms are not explained (TCCON COCCON MAGIC FORUM). With some this might be fine (i.e. citation to the network main paper), but with other not.
You write a lot about your Model and Retrieval, but in the end it is a bit vague to me what exactly you use in your state vector (for the gases: mixing ratios, concentrations, column densities) and in your measurement vector (radiances, transmittance, ...) - I would consider this the most relevant information on a higher level of how your retrieval is designed.
Regarding your results of the information content analysis of the spectrum (Figure 4 and 5), I am not completely convinced, since I miss a few points in the discussion:
- As I understand it, you do all of this analysis in some type of absorbance space - but to get there from measured radiances, a "background channel" is definitely needed - which I do not see represented in your results.
- You are only considering the CO2 information at the moment, but the large advantage of using a widely tunable laser is in my opinion that you can measure full rot-vib bands and get constraints on the temperature - which is degenerated with the gas amount for a single line or a few close ones. Could you see any improvements here?
- Are you proposing to simply limit the used channels in a retrieval or also to limit the measured spectral bandwidth?
Line-by-line comments:
9f: I find the first sentence a bit vague and not adding anything of value – the same could be said about many methods.
10f: Semantically wrong in my opinion. Heterodyne detection is a method, not an instrument and thus can not be “transportable” or similar.
23: I would say the “radiative impacts on the atmosphere” of higher greenhouse gas (GHG) concentrations is well sorted since a few decades and “studying the effects of climate change” with an instrument as presented sounds to me at best like the analysis of changes in biological sources and sinks of GHGs.
24ff (“In addition to [..]”): This is quite a generic reference with an even broader reference. If you cite the latest IPCC report for something like this, I would ask you to be more precise where in the thousands of pages you get that from.
31: I wouldn’t call the logistical requirements of COCCON “low”. While the EM27/SUN is portable (unlike the instruments of the TCCON network), the logistics behind operating a network of them, ensuring the comparability of the instruments, etc. is quite substantial and an achievement.
35: If you argue via cost effectiveness, could you give a rough number/price?
35: What sensitivity limits are you talking about?
55: Since lock-in amplifiers are not necessarily know to the everybody in the target group of this journal I would ask you to add half a sentence explaining why you modulate the light source with a chopper.
68: Can you also give details on the product concerning the “square law detector”? I think this could help avoiding misunderstandings, since many readers will think "photo diode" when reading this in the context of this journal and the topic.
77: I think this equation is not clear at all, even after reading the cited reference. At a minimum, the chosen definition for SNR should be reiterated and the relevant assumptions stated (relative strengths of signals, shot noise limits, etc.).
82: You state the theoretical SNR and then introduce your measurements, but I do not find how well the actual measured SNR compares to the theoretical one.
84: “an information content study”?
90: Why a Gaussian line profile? This would be rather unusual (and wrong). You should at least use Voight, but even this is not necessarily up to current standards.
94: What is the LATMOS function?
103f: To make any statement about the consistency a plot of the residuals between measurement and forward model is required.
Figure 2: please be more clear in the layout and caption of the figure what is measured data and what is simulation.
144: I “I” a unity matrix?
146f: You say that “Sm is calculated [..]” but then proceed to give an equation for Smeas..
161: I don’t get the division by 100 in the equation. Looks to me like a conversion from percent to a straight number, but perror is sometimes given as absolute value including units (i.e. for the temperature) in your table.
190: To my understanding 10° measurements are rather unrealistic for high quality direct sun observations.
200f: In a sense, this is obvious - higher up, lower pressure, less molecules in a fixed height layer. This is one of the reasons why the atmospheric retrieval codes I am familiar with use equidistant levels in pressure, which result in (roughly) equal amounts of molecules per layer. So here, it is unclear to me, how much if not all of the described effect is due to the lower number of molecules.
208: Maybe change the order of the tables? Table 4 is needed before Table 2.
Figure 3: Please add a better explanation (maybe linked to your formalism for A) what the different lines are.
235: Optical path difference? You are talking about a longer path in the atmosphere I assume?
Table 3: What definition of SNR is utilized here? Is it comparable between the different measurements/works? Also: What CHRIS is remains unclear to somebody not familiar with the corresponding paper.
Figure 5: Axis ticks very hard to read.
Citation: https://doi.org/10.5194/egusphere-2025-250-RC1 -
AC2: 'Reply on RC1', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC2-supplement.pdf
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EC1: 'Comment on egusphere-2025-250', Frank Hase, 26 May 2025
This work by El Kattar et al. describes a portable NIR LHR and investigates the expected performance of such kind of spectrometer by performing an information content analysis. LHRs offer the prospect of replacing or at least complementing traditional FTIR approaches, so this is a very relevant study, which fits well into the scope of AMT. However, the current manuscript requires substantial revision before final publication can be considered.
Comments:
Section 3.1, line 90: I can hardly believe a Gaussian line shape is assumed for modelling the absorption lines, I guess you refer to the Voigt profile?
Section 3.1, line 102: please rephrase: “ …. while TCCON a-priori information is used for CO2 and H2O atmospheric profiles.”
Figure 2: The calculated H2O signatures seem undetectable in the measured spectrum - is the slant column used for the calculation not matched properly? In the figure description, please specify SZA of the observation and total integration time of the measurement shown.
Section 3.2: in the opening section of this treatment, information needs to be given concerning which quantities are fitted in the retrieval (please add a table listing all components of the state vector): I guess in addition to the gas mixing ratios of CO2 and H2O, spectral shift (or scale) is fitted? Further fit variables are needed for describing the continuum background level. Is the solar spectral abscissa scale fitted (usually required in spectrally high-res measurements to compensate for residual LOS errors)?
Section 3.2, line 126: “the ith measurement” -> “the ith spectral channel in the measured spectrum”
Section 3.3: unfortunately, the construction of the a-priori (see table 1) is so oversimplified that I doubt any useful conclusions can be drawn from the current information content analysis. If one wants to compare the expected performance of the presented LHR with existing FTIR setups in a sensible manner, the a-priori covariance matrix needs to be far more realistic. Moreover, the information content analysis needs to discuss explicitly the expected errors on column-averaged abundances, which are the products of current networks. Note that the current networks do not provide gas columns, but XGAS values, which are constructed with the help of co-observed O2 columns. Your claim “The LHR exhibits unique advantages … in retrieving gas columns with better vertical discretization [should be: resolution]. It is therefore a promising alternative instrument for local scale measurements or for satellite validation”. This might be correct, but needs to be supported by the results of information content analysis. In the application context you refer to in the manuscript (especially satellite validation), high vertical resolution is mainly useful via improving the reconstruction of XGAS amounts over current techniques. (It needs to be kept in mind that the satellite also measures XGAS.)
For achieving a meaningful comparison with current state-of-the-art, I would suggest to proceed in the following manner:
(1) construct sensible S_a matrices for CO2, H2O, and T. Note that the relevant variability here to be reported in S_a is the variability between the actual profile and the TCCON a-priori. This S_a matrix for CO2 can be constructed from aircore launches (the French community is quite active with this technique, so a sufficient amount of data should be available for constructing an S_a matrix). The equivalent matrices for H2O and T can be derived from meteorological soundings, ideally launches which were not used in the model assimilation underlying TCCON a-prioris (perhaps H2O and T are by-products of aircore launches anyway?). When constructing the S_a matrices, it is crucially important to maintain the covariances, which inform about characteristic lengths of variability along the vertical. Only by maintaining the diagonal elements a meaningful S_a matrix is constructed.
(2) For the performance comparison with TCCON and COCCON for the XGAS values, the propagation of T errors into a O2 retrieval from the 1.26 um band needs to be included. This will alter (expectedly improve) the uncertainty budget for the target quantity XCO2, as this is calculated using the ratio of CO2 and O2 columns. This error compensation is lacking in the LHR approach. Moreover, note that SZA errors cancel out in this rationing approach, so in the discussion of model errors, the resulting error contribution for the LHR needs to be estimated (from the assumed SZA errors).
(3) A further important model parameter is the ground pressure. Ideally, it should be included in the error analysis, as the sensitivity of the LHR very likely differs from that of TCCON and COCCON due to the high spectral resolution and due to the fact, that there is no rationing over the O2 column. But if you clearly state in the text that you assume the availability of an ideal sensor, one might skip this item.
Using (1) and (2) and your error propagation equations, you can realistically establish the desired performance comparison between the LHR and current techniques. I would expect that the LHR is superior wrt the smoothing error, while the current networks might be more robust wrt the impact of model parameter errors (T and SZA). With respect to the smoothing error, it might be interesting for TCCON + COCCON to work out the smoothing error both for the operational setup (scaling retrieval) and a possible future data processing which performs a profile retrieval fit of CO2. The latter result would specifically reveal the improvement introduced by the high spectral resolution achieved by the LHR. If, however, you feel this is beyond the scope of your work, restrict the investigation to the operational setup.
In table 3, please add integration times, otherwise the comparison of SNR figures is not meaningful.
In table 4, the column errors seem unrealistically large to me for all instruments. It therefore would be good to split the reported error into different contributions (spectral noise, model parameters, smoothing). This would make transparent which error source drives the total budget and would allow to explicitly verify at least the calculated noise error contribution, as this can be easily deduced from data retrieved from actual measurements.
The treatment provided in section 4 needs to be refined. The authors claim that from this investigation, the preferred CO2 channels to be measured can be deduced, saving observation and data analysis time. This is in principle correct, but we need to realize that the presented LHR is operated as a ground-based solar absorption spectrometer. In this configuration, a model for the continuum background level needs to be included in the fit (because a solar reference measurement outside of the atmosphere is not doable). This in turn requires a sufficient number of background channels (spectral positions largely free of absorption) to be included both in the measurement and in the fit. The analysis, however, suggests using only channels with strong CO2 absorption, which seems unrealistic.
Citation: https://doi.org/10.5194/egusphere-2025-250-EC1 -
AC3: 'Reply on EC1', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC3-supplement.pdf
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AC3: 'Reply on EC1', El Kattar Marie-Thérèse, 25 Jul 2025
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RC2: 'Comment on egusphere-2025-250', Anonymous Referee #2, 01 Jul 2025
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AC1: 'Reply on RC2', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC1-supplement.pdf
-
AC1: 'Reply on RC2', El Kattar Marie-Thérèse, 25 Jul 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-250', Anonymous Referee #1, 08 May 2025
The manuscript describes the development and analysis of a laser heterodyne radiometer (LHR) using a widely tunable diode laser. This allows – unlike most recent LHR systems for trace gas remote sensing, which typically rely on quantum cascade lasers or similar – the measurements of full rotation-vibrations bands of molecules like CO2 and an easier access to multiple species. The authors combine this with the high spectral resolution typical for LHRs to retrieve vertical profiles of CO2 in solar occultation measurements. They follow this up with comprehensive analysis of the information content within the spectrum and of the error budget. I consider the content highly significant, for the current development of LHR systems for remote sensing in the atmosphere, and the manuscript well written. Further, I appreciate the thorough addressing of information content and channel selection - since it is time consuming it is often done in an ad-hoc trial and error way (unless maybe for large projects), so it is nice to see a practical example in recent literature. However, I think there are a few points were additional clarifications and minor improvements would allow the manuscript to be equally accessible for those members of the community with a stronger background in atmospheric remote sensing as well as for those with their main background in lab spectroscopy. So I would like to propose the following general remarks and comments for the authors to consider moving forwards and point out a few clarifications which seem necessary to me. Since I do not bring a specific expertise on the matter of the details of the retrieval, my comments will be mostly limited to technical corrections and questions of understanding in the corresponding sections.
General Remarks:
Many acronyms are not explained (TCCON COCCON MAGIC FORUM). With some this might be fine (i.e. citation to the network main paper), but with other not.
You write a lot about your Model and Retrieval, but in the end it is a bit vague to me what exactly you use in your state vector (for the gases: mixing ratios, concentrations, column densities) and in your measurement vector (radiances, transmittance, ...) - I would consider this the most relevant information on a higher level of how your retrieval is designed.
Regarding your results of the information content analysis of the spectrum (Figure 4 and 5), I am not completely convinced, since I miss a few points in the discussion:
- As I understand it, you do all of this analysis in some type of absorbance space - but to get there from measured radiances, a "background channel" is definitely needed - which I do not see represented in your results.
- You are only considering the CO2 information at the moment, but the large advantage of using a widely tunable laser is in my opinion that you can measure full rot-vib bands and get constraints on the temperature - which is degenerated with the gas amount for a single line or a few close ones. Could you see any improvements here?
- Are you proposing to simply limit the used channels in a retrieval or also to limit the measured spectral bandwidth?
Line-by-line comments:
9f: I find the first sentence a bit vague and not adding anything of value – the same could be said about many methods.
10f: Semantically wrong in my opinion. Heterodyne detection is a method, not an instrument and thus can not be “transportable” or similar.
23: I would say the “radiative impacts on the atmosphere” of higher greenhouse gas (GHG) concentrations is well sorted since a few decades and “studying the effects of climate change” with an instrument as presented sounds to me at best like the analysis of changes in biological sources and sinks of GHGs.
24ff (“In addition to [..]”): This is quite a generic reference with an even broader reference. If you cite the latest IPCC report for something like this, I would ask you to be more precise where in the thousands of pages you get that from.
31: I wouldn’t call the logistical requirements of COCCON “low”. While the EM27/SUN is portable (unlike the instruments of the TCCON network), the logistics behind operating a network of them, ensuring the comparability of the instruments, etc. is quite substantial and an achievement.
35: If you argue via cost effectiveness, could you give a rough number/price?
35: What sensitivity limits are you talking about?
55: Since lock-in amplifiers are not necessarily know to the everybody in the target group of this journal I would ask you to add half a sentence explaining why you modulate the light source with a chopper.
68: Can you also give details on the product concerning the “square law detector”? I think this could help avoiding misunderstandings, since many readers will think "photo diode" when reading this in the context of this journal and the topic.
77: I think this equation is not clear at all, even after reading the cited reference. At a minimum, the chosen definition for SNR should be reiterated and the relevant assumptions stated (relative strengths of signals, shot noise limits, etc.).
82: You state the theoretical SNR and then introduce your measurements, but I do not find how well the actual measured SNR compares to the theoretical one.
84: “an information content study”?
90: Why a Gaussian line profile? This would be rather unusual (and wrong). You should at least use Voight, but even this is not necessarily up to current standards.
94: What is the LATMOS function?
103f: To make any statement about the consistency a plot of the residuals between measurement and forward model is required.
Figure 2: please be more clear in the layout and caption of the figure what is measured data and what is simulation.
144: I “I” a unity matrix?
146f: You say that “Sm is calculated [..]” but then proceed to give an equation for Smeas..
161: I don’t get the division by 100 in the equation. Looks to me like a conversion from percent to a straight number, but perror is sometimes given as absolute value including units (i.e. for the temperature) in your table.
190: To my understanding 10° measurements are rather unrealistic for high quality direct sun observations.
200f: In a sense, this is obvious - higher up, lower pressure, less molecules in a fixed height layer. This is one of the reasons why the atmospheric retrieval codes I am familiar with use equidistant levels in pressure, which result in (roughly) equal amounts of molecules per layer. So here, it is unclear to me, how much if not all of the described effect is due to the lower number of molecules.
208: Maybe change the order of the tables? Table 4 is needed before Table 2.
Figure 3: Please add a better explanation (maybe linked to your formalism for A) what the different lines are.
235: Optical path difference? You are talking about a longer path in the atmosphere I assume?
Table 3: What definition of SNR is utilized here? Is it comparable between the different measurements/works? Also: What CHRIS is remains unclear to somebody not familiar with the corresponding paper.
Figure 5: Axis ticks very hard to read.
Citation: https://doi.org/10.5194/egusphere-2025-250-RC1 -
AC2: 'Reply on RC1', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC2-supplement.pdf
-
EC1: 'Comment on egusphere-2025-250', Frank Hase, 26 May 2025
This work by El Kattar et al. describes a portable NIR LHR and investigates the expected performance of such kind of spectrometer by performing an information content analysis. LHRs offer the prospect of replacing or at least complementing traditional FTIR approaches, so this is a very relevant study, which fits well into the scope of AMT. However, the current manuscript requires substantial revision before final publication can be considered.
Comments:
Section 3.1, line 90: I can hardly believe a Gaussian line shape is assumed for modelling the absorption lines, I guess you refer to the Voigt profile?
Section 3.1, line 102: please rephrase: “ …. while TCCON a-priori information is used for CO2 and H2O atmospheric profiles.”
Figure 2: The calculated H2O signatures seem undetectable in the measured spectrum - is the slant column used for the calculation not matched properly? In the figure description, please specify SZA of the observation and total integration time of the measurement shown.
Section 3.2: in the opening section of this treatment, information needs to be given concerning which quantities are fitted in the retrieval (please add a table listing all components of the state vector): I guess in addition to the gas mixing ratios of CO2 and H2O, spectral shift (or scale) is fitted? Further fit variables are needed for describing the continuum background level. Is the solar spectral abscissa scale fitted (usually required in spectrally high-res measurements to compensate for residual LOS errors)?
Section 3.2, line 126: “the ith measurement” -> “the ith spectral channel in the measured spectrum”
Section 3.3: unfortunately, the construction of the a-priori (see table 1) is so oversimplified that I doubt any useful conclusions can be drawn from the current information content analysis. If one wants to compare the expected performance of the presented LHR with existing FTIR setups in a sensible manner, the a-priori covariance matrix needs to be far more realistic. Moreover, the information content analysis needs to discuss explicitly the expected errors on column-averaged abundances, which are the products of current networks. Note that the current networks do not provide gas columns, but XGAS values, which are constructed with the help of co-observed O2 columns. Your claim “The LHR exhibits unique advantages … in retrieving gas columns with better vertical discretization [should be: resolution]. It is therefore a promising alternative instrument for local scale measurements or for satellite validation”. This might be correct, but needs to be supported by the results of information content analysis. In the application context you refer to in the manuscript (especially satellite validation), high vertical resolution is mainly useful via improving the reconstruction of XGAS amounts over current techniques. (It needs to be kept in mind that the satellite also measures XGAS.)
For achieving a meaningful comparison with current state-of-the-art, I would suggest to proceed in the following manner:
(1) construct sensible S_a matrices for CO2, H2O, and T. Note that the relevant variability here to be reported in S_a is the variability between the actual profile and the TCCON a-priori. This S_a matrix for CO2 can be constructed from aircore launches (the French community is quite active with this technique, so a sufficient amount of data should be available for constructing an S_a matrix). The equivalent matrices for H2O and T can be derived from meteorological soundings, ideally launches which were not used in the model assimilation underlying TCCON a-prioris (perhaps H2O and T are by-products of aircore launches anyway?). When constructing the S_a matrices, it is crucially important to maintain the covariances, which inform about characteristic lengths of variability along the vertical. Only by maintaining the diagonal elements a meaningful S_a matrix is constructed.
(2) For the performance comparison with TCCON and COCCON for the XGAS values, the propagation of T errors into a O2 retrieval from the 1.26 um band needs to be included. This will alter (expectedly improve) the uncertainty budget for the target quantity XCO2, as this is calculated using the ratio of CO2 and O2 columns. This error compensation is lacking in the LHR approach. Moreover, note that SZA errors cancel out in this rationing approach, so in the discussion of model errors, the resulting error contribution for the LHR needs to be estimated (from the assumed SZA errors).
(3) A further important model parameter is the ground pressure. Ideally, it should be included in the error analysis, as the sensitivity of the LHR very likely differs from that of TCCON and COCCON due to the high spectral resolution and due to the fact, that there is no rationing over the O2 column. But if you clearly state in the text that you assume the availability of an ideal sensor, one might skip this item.
Using (1) and (2) and your error propagation equations, you can realistically establish the desired performance comparison between the LHR and current techniques. I would expect that the LHR is superior wrt the smoothing error, while the current networks might be more robust wrt the impact of model parameter errors (T and SZA). With respect to the smoothing error, it might be interesting for TCCON + COCCON to work out the smoothing error both for the operational setup (scaling retrieval) and a possible future data processing which performs a profile retrieval fit of CO2. The latter result would specifically reveal the improvement introduced by the high spectral resolution achieved by the LHR. If, however, you feel this is beyond the scope of your work, restrict the investigation to the operational setup.
In table 3, please add integration times, otherwise the comparison of SNR figures is not meaningful.
In table 4, the column errors seem unrealistically large to me for all instruments. It therefore would be good to split the reported error into different contributions (spectral noise, model parameters, smoothing). This would make transparent which error source drives the total budget and would allow to explicitly verify at least the calculated noise error contribution, as this can be easily deduced from data retrieved from actual measurements.
The treatment provided in section 4 needs to be refined. The authors claim that from this investigation, the preferred CO2 channels to be measured can be deduced, saving observation and data analysis time. This is in principle correct, but we need to realize that the presented LHR is operated as a ground-based solar absorption spectrometer. In this configuration, a model for the continuum background level needs to be included in the fit (because a solar reference measurement outside of the atmosphere is not doable). This in turn requires a sufficient number of background channels (spectral positions largely free of absorption) to be included both in the measurement and in the fit. The analysis, however, suggests using only channels with strong CO2 absorption, which seems unrealistic.
Citation: https://doi.org/10.5194/egusphere-2025-250-EC1 -
AC3: 'Reply on EC1', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC3-supplement.pdf
-
AC3: 'Reply on EC1', El Kattar Marie-Thérèse, 25 Jul 2025
-
RC2: 'Comment on egusphere-2025-250', Anonymous Referee #2, 01 Jul 2025
-
AC1: 'Reply on RC2', El Kattar Marie-Thérèse, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-250/egusphere-2025-250-AC1-supplement.pdf
-
AC1: 'Reply on RC2', El Kattar Marie-Thérèse, 25 Jul 2025
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Marie Thérèse El Kattar
Tingting Wei
Aditya Saxena
Hervé Herbin
Weidong Chen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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