Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Villeneuve, Ethel; Chambon, Philippe; Fourrié, Nadia

doi:https://doi.org/10.5194/egusphere-2023-446

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https://doi.org/10.5194/egusphere-2023-446

Preprints

14 Mar 2023

| 14 Mar 2023

Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié

Abstract. In cloudy situations, infrared and microwave observations are complementary, with infrared observations being sensitive to the small cloud droplets and ice particles and microwave observations sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g. Kidd and Levizzani, 2022). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer model RTTOV v13 are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated; (ii) with only one source of errors considered at a time; (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of fifteen days in summer and fifteen days in winter over the Atlantic ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of both spectral range strengths leading to accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that even though the errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, in all cases explored, their combination remains beneficial.

Received: 10 Mar 2023 – Discussion started: 14 Mar 2023

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12 Jun 2024

| Highlight paper

Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié

Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024,https://doi.org/10.5194/amt-17-3567-2024, 2024

Short summary Executive editor

Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-446', Anonymous Referee #1, 13 Apr 2023

Review of Villeneuve et al. – Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

The authors present a synthetic study of retrieval synergy between microwave, infrared, and sub-mm observations for constraining ice hydrometeors. This paper is well-written and relevant for publication in this journal. The study is well-constructed, the methodology is explained well, and reasonable conclusions are drawn about the synergistic value of these observations for frozen hydrometeors, while being clear about the caveats and shortcomings of this synthetic approach. My recommendation is for publication after some minor corrections.

One area where the manuscript is a little lacking regards the context of other synergistic studies in the literature. For instance, there are recent studies examining the combined use of MWI and ICI (https://amt.copernicus.org/articles/13/4219/2020/) and also active sensors, including using real observations (https://amt.copernicus.org/articles/15/677/2022/). As their conclusions regarding the importance of particle shape and RT errors in general are similar to the ones of this study, it is perhaps worth mentioning in the discussion section or the introduction to provide some context for readers. Other studies have also probed the importance of microphysical assumptions for different wavelengths (https://amt.copernicus.org/articles/14/5369/2021/ https://amt.copernicus.org/articles/13/501/2020/), again with conclusions that seem compatible with this study. There are also Bayesian-based studies on ice hydrometeor retrieval synergy (https://amt.copernicus.org/articles/15/927/2022/), including several others that examine radar/radiometer synergy from CloudSat and GPM. The authors’ paper is more focused on eventual assimilation, but some context on retrieval focussed studies could still be helpful. The papers listed above are just from AMT, and surely there are others elsewhere.

Specific comments:
L11 – “takes advantage of both spectral range strengths” could perhaps be rewritten as “takes advantage of the strengths of both spectral ranges”
L13 – This last sentence is not specific enough and may need to be reworded. For instance, does “the errors” mean “the radiative transfer and numerical modelling errors”? Does “their combination” mean the combination of different sensors? It’s also not clear that this is true in “all cases explored” because the graupel combined retrieval performed worse than MWI when it came to graupel.
L21 – “a significant information content” is quite vague, suggest rewording
L26 – Worth spelling out what all-sky means for readers
L33 – Suggest changing “at discussing” to “to explore”
L50 – This is nitpicking, but sub-mm is greater than 300GHz, so ICI will technically measure MW and sub-mm wavelengths
L51 – Again a technicality, but there have been short-lived sensors measuring at sub-mm, and AWS might be launched before ICI, so this statement could be toned down.
Table 2 – It’s a bit confusing how “OBS FG” is shown. Does this mean OBS in rows and FG in columns? Could make this clearer.
L113 – See tables 4 & 5, presumably?
Tables 3, 4, 5 – Would it be possible to combine into one table? This would make it easier to compare values across sensors.
L145 – Tables 6, 7, & 8, presumably?
Tables 6, 7, 8 – Same as above, could these be combined?
L211 – It would be helpful to provide more detail here to explain exactly why this validation comparison is done. Right now it feels quite implicit and the reasoning is split up (L234, L355), but it would be helpful to spell out exactly why the validation was performed at the beginning of this section.
Figures 2 & 3 – y-axis should be STD and mean?
Figures 4, 6, 8 – Here the y-axis could be reasonably cut off at 100hPa, as presumably the significance differences are spurious noise above this level.
L271 – Reword “instrumental synergy” to something like “synergy of the instruments”
Figures 5, 7 9 – Here the x-axis label of “abs error” was quite confusing for me. Isn’t this the difference definition given in Section 2.5? Also a typo in first panel of ‘mTR’ rather than ‘mRT’
L306 – Why is this given halfway through the results section? It would make much more sense at the beginning of Section 4.
L318 – Worth mentioning that the FCI curves are again absent in Fig. 9, as stated for Fig. 7

Citation: https://doi.org/10.5194/egusphere-2023-446-RC1
- AC1: 'Reply on RC1', Ethel Villeneuve, 14 Nov 2023
  
  The authors thank the reviewer for their comment that helped us improve the manuscript by adding clarification.
  The detailed response is attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-446-AC1
RC2:
'Comment on egusphere-2023-446', Anonymous Referee #2, 17 Oct 2023

Review comments for “Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework” by Villeneuve et al.

This work thoroughly assessed the benefits of combining passive thermal infrared (TIR), sub-millimeter (sub-mm) and microwave (MW) instruments in retrieving frozen hydrometeors (cloud ice, snow, and graupel) using a Bayesian retrieval framework and a regional model outputs as the input “truth” for retrieval and validation. What’s more, this work also evaluated two retrieval error sources from radiative transfer model microphysics assumption (mRT experiments) and from model microphysics parameterization schemes (mMOD experiments) that might lead to degradation of the synergy. Three upcoming spaceborne instruments (FCI, ICI and MWI) are brought in for specific channel frequency settings, but their mis-match footprint, view-angle, etc., were not considered yet in this paper. The major conclusion of this work is that synergy overall is better than using any of the three instruments individually for retrieving cloud ice and snow profiles, but not necessarily for graupel. Retrieval performance (i.e., error) tends to be more sensitive to model microphysics scheme parameters than to which type of ice particle shape that is chosen (again, opposite for graupel). The discussions speculate some possible explanations for the behaviors error metrics (mainly standard deviation of inversion error compared with using single instrument). Some other error sources that are important but not considered in this work are laid out in the end.

Overall I think this is a solid paper that involves extensive efforts and a sound framework for testing and validation. Therefore, I strongly support the publication of this work eventually. However, I do think there are some important issues that need to be clarified, and some more potential experiments need to be considered. I wouldn’t put a “major revision” as my recommendation, as the idea, methodology, execution and result presentation do not have serious issues and worth some great appreciation.

The overarching goals are a bit ambitious in design: trying to assess two important problems (i.e., synergy, and synergy sensitivity) in one paper. I would rather consider separating into two companion papers so each one can focus on one problem which gets a chance to be more elaborated. Right now the first 14 pages are spent for describing experiment settings, while Page 15 – 20 are for presenting the results, which followed with one page very brief discussion. I feel it’s not an ideal paper structure. Below are my major concerns:

(1) For assessing the synergy benefit, CA and SCP are introduced in Equation (5) and (8) for IR and MW, respectively. Maybe these are some standard parameters that data assimilation people used a lot already, but for a retrieval person like me, I lost clue on why using these two metrics, what are their physical meanings, and why it’s inconsistent between TIR and MW. Substantial explanations and discussions are needed here. For example, in Fig. 2 why the average of errors are small positive for noERR and mRT, larger and negative for mMOD, but the standard deviation are comparable in size? Shouldn’t the CA error increases for thicker clouds (I guessed from the grey bar, which I assume correspond to the number of cases)? Would you concern about using STD difference to assess your synergy performance when the bias are not even the same sign?
(2) For mMOD experiments, I never understand how tuning so many parameters can give you one final assessment value at the end? Did you carry out two sets of experiments, one using the lower values in your Table 11, one using the higher bound values, and then computing their difference against your noERR results? As acknowledged in this paper, it is expected to see significant compensations among different parameters. It worths at least one paragraph here or better an appendix section to describe details about mMOD experiment settings.

(3) I’m having some issue with the settings of mRT experiments, in particular, the selection of snow particle shape for noERR and mRT. 183 GHz and sub-mm channels are particularly sensitive to snow particle shape, and many previous observations using limited field campaigns or satellite observations have demonstrated that it’s more proper to use “Evans snow” or “Liu’s sector snow” for snow, and the largest discrepancies come from “soft spheroid” (e.g., Ekelund et al., 2020; Gong et al., 2021). The two snow shapes usually produce quite similar results for sub-mm and MW channels. This is the part that I feel uncomfortable for the settings and suggest to change. For “graupel”, usually we use “8-column aggregates” in ARTS, but I guess that’s not a serious issue as I expect sub-mm and high-frequency MW bands to be saturated to graupels quickly anyway.

I’m not surprised to see graupel retrieval error is not sensitive to mMOD, and agree with the authors that cumulus parameterization scheme is probably that matters for graupel instead of microphysics details in cloud ice and snow (still surprised me that tuning entrainment rate seems to not work). However, it’s also worth noting in the paper that none of these channels are really sensitive to graupel, so it’s expected that the synergy of the three would get things worse. I might have overlooked this point in the manuscript, or I feel it is not emphasized enough in the discussion related to Fig. 8 & Fig. 9.

(4) Another relatively important issue that was overlooked in mRT experiment is the assumption of particle size distribution (PSD), which matters a lot for sub-mm and MW channels. There is a variety of PSD choices in ARTS, and I think it worths to be considered in the mRT experiments.

Minor issues:
Fig. 5, 7 & 9: I’d strongly suggest you to include a mean IWC profile with standard deviation as a reference panel in addition to the current ones. This is helpful for readers to at least visually assess the percentage error of improvements. For example, I found it’s very interesting to see mRT improves cloud ice retrieval when you focus on snow (Fig. 7a, cyan vs. green). By the way, mRT seems to be mis-spelled as “mTR” in all three figure sub-titles.

Section 5.3: Can’t agree you more with your point #3 and #4. For #3, please consider citing Barlakas and Eriksson (2020). It’s a nice paper focusing on sub-grid variability for sub-mm radiometer retrievals. For models with 5 km resolution, it’s comparable to footprint size of these sensors but facing similar order of sub-grid variability. For #4, it is real when it comes to the real algorithm design for combined algorithms, which worths another paper to discuss and #3 and #4 are tightly tied. (guess this is just my comment)

References:
Barlakas and Eriksson (2020): https://doi.org/10.3390/rs12030531
Ekelund et al. (2020): https://doi.org/10.5194/amt-13-501-2020
Gong et al. (2021): https://doi.org/10.5194/essd-13-5369-2021

Citation: https://doi.org/10.5194/egusphere-2023-446-RC2
- AC2: 'Reply on RC2', Ethel Villeneuve, 14 Nov 2023
  
  The authors would like to thank the reviewer for taking the time to make comment on the manuscript. It helped us to clarify it.
  The detailed response is attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-446-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-446', Anonymous Referee #1, 13 Apr 2023

Review of Villeneuve et al. – Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

The authors present a synthetic study of retrieval synergy between microwave, infrared, and sub-mm observations for constraining ice hydrometeors. This paper is well-written and relevant for publication in this journal. The study is well-constructed, the methodology is explained well, and reasonable conclusions are drawn about the synergistic value of these observations for frozen hydrometeors, while being clear about the caveats and shortcomings of this synthetic approach. My recommendation is for publication after some minor corrections.

One area where the manuscript is a little lacking regards the context of other synergistic studies in the literature. For instance, there are recent studies examining the combined use of MWI and ICI (https://amt.copernicus.org/articles/13/4219/2020/) and also active sensors, including using real observations (https://amt.copernicus.org/articles/15/677/2022/). As their conclusions regarding the importance of particle shape and RT errors in general are similar to the ones of this study, it is perhaps worth mentioning in the discussion section or the introduction to provide some context for readers. Other studies have also probed the importance of microphysical assumptions for different wavelengths (https://amt.copernicus.org/articles/14/5369/2021/ https://amt.copernicus.org/articles/13/501/2020/), again with conclusions that seem compatible with this study. There are also Bayesian-based studies on ice hydrometeor retrieval synergy (https://amt.copernicus.org/articles/15/927/2022/), including several others that examine radar/radiometer synergy from CloudSat and GPM. The authors’ paper is more focused on eventual assimilation, but some context on retrieval focussed studies could still be helpful. The papers listed above are just from AMT, and surely there are others elsewhere.

Specific comments:
L11 – “takes advantage of both spectral range strengths” could perhaps be rewritten as “takes advantage of the strengths of both spectral ranges”
L13 – This last sentence is not specific enough and may need to be reworded. For instance, does “the errors” mean “the radiative transfer and numerical modelling errors”? Does “their combination” mean the combination of different sensors? It’s also not clear that this is true in “all cases explored” because the graupel combined retrieval performed worse than MWI when it came to graupel.
L21 – “a significant information content” is quite vague, suggest rewording
L26 – Worth spelling out what all-sky means for readers
L33 – Suggest changing “at discussing” to “to explore”
L50 – This is nitpicking, but sub-mm is greater than 300GHz, so ICI will technically measure MW and sub-mm wavelengths
L51 – Again a technicality, but there have been short-lived sensors measuring at sub-mm, and AWS might be launched before ICI, so this statement could be toned down.
Table 2 – It’s a bit confusing how “OBS FG” is shown. Does this mean OBS in rows and FG in columns? Could make this clearer.
L113 – See tables 4 & 5, presumably?
Tables 3, 4, 5 – Would it be possible to combine into one table? This would make it easier to compare values across sensors.
L145 – Tables 6, 7, & 8, presumably?
Tables 6, 7, 8 – Same as above, could these be combined?
L211 – It would be helpful to provide more detail here to explain exactly why this validation comparison is done. Right now it feels quite implicit and the reasoning is split up (L234, L355), but it would be helpful to spell out exactly why the validation was performed at the beginning of this section.
Figures 2 & 3 – y-axis should be STD and mean?
Figures 4, 6, 8 – Here the y-axis could be reasonably cut off at 100hPa, as presumably the significance differences are spurious noise above this level.
L271 – Reword “instrumental synergy” to something like “synergy of the instruments”
Figures 5, 7 9 – Here the x-axis label of “abs error” was quite confusing for me. Isn’t this the difference definition given in Section 2.5? Also a typo in first panel of ‘mTR’ rather than ‘mRT’
L306 – Why is this given halfway through the results section? It would make much more sense at the beginning of Section 4.
L318 – Worth mentioning that the FCI curves are again absent in Fig. 9, as stated for Fig. 7

Citation: https://doi.org/10.5194/egusphere-2023-446-RC1
- AC1: 'Reply on RC1', Ethel Villeneuve, 14 Nov 2023
  
  The authors thank the reviewer for their comment that helped us improve the manuscript by adding clarification.
  The detailed response is attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-446-AC1
RC2:
'Comment on egusphere-2023-446', Anonymous Referee #2, 17 Oct 2023

Review comments for “Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework” by Villeneuve et al.

This work thoroughly assessed the benefits of combining passive thermal infrared (TIR), sub-millimeter (sub-mm) and microwave (MW) instruments in retrieving frozen hydrometeors (cloud ice, snow, and graupel) using a Bayesian retrieval framework and a regional model outputs as the input “truth” for retrieval and validation. What’s more, this work also evaluated two retrieval error sources from radiative transfer model microphysics assumption (mRT experiments) and from model microphysics parameterization schemes (mMOD experiments) that might lead to degradation of the synergy. Three upcoming spaceborne instruments (FCI, ICI and MWI) are brought in for specific channel frequency settings, but their mis-match footprint, view-angle, etc., were not considered yet in this paper. The major conclusion of this work is that synergy overall is better than using any of the three instruments individually for retrieving cloud ice and snow profiles, but not necessarily for graupel. Retrieval performance (i.e., error) tends to be more sensitive to model microphysics scheme parameters than to which type of ice particle shape that is chosen (again, opposite for graupel). The discussions speculate some possible explanations for the behaviors error metrics (mainly standard deviation of inversion error compared with using single instrument). Some other error sources that are important but not considered in this work are laid out in the end.

Overall I think this is a solid paper that involves extensive efforts and a sound framework for testing and validation. Therefore, I strongly support the publication of this work eventually. However, I do think there are some important issues that need to be clarified, and some more potential experiments need to be considered. I wouldn’t put a “major revision” as my recommendation, as the idea, methodology, execution and result presentation do not have serious issues and worth some great appreciation.

The overarching goals are a bit ambitious in design: trying to assess two important problems (i.e., synergy, and synergy sensitivity) in one paper. I would rather consider separating into two companion papers so each one can focus on one problem which gets a chance to be more elaborated. Right now the first 14 pages are spent for describing experiment settings, while Page 15 – 20 are for presenting the results, which followed with one page very brief discussion. I feel it’s not an ideal paper structure. Below are my major concerns:

(1) For assessing the synergy benefit, CA and SCP are introduced in Equation (5) and (8) for IR and MW, respectively. Maybe these are some standard parameters that data assimilation people used a lot already, but for a retrieval person like me, I lost clue on why using these two metrics, what are their physical meanings, and why it’s inconsistent between TIR and MW. Substantial explanations and discussions are needed here. For example, in Fig. 2 why the average of errors are small positive for noERR and mRT, larger and negative for mMOD, but the standard deviation are comparable in size? Shouldn’t the CA error increases for thicker clouds (I guessed from the grey bar, which I assume correspond to the number of cases)? Would you concern about using STD difference to assess your synergy performance when the bias are not even the same sign?
(2) For mMOD experiments, I never understand how tuning so many parameters can give you one final assessment value at the end? Did you carry out two sets of experiments, one using the lower values in your Table 11, one using the higher bound values, and then computing their difference against your noERR results? As acknowledged in this paper, it is expected to see significant compensations among different parameters. It worths at least one paragraph here or better an appendix section to describe details about mMOD experiment settings.

(3) I’m having some issue with the settings of mRT experiments, in particular, the selection of snow particle shape for noERR and mRT. 183 GHz and sub-mm channels are particularly sensitive to snow particle shape, and many previous observations using limited field campaigns or satellite observations have demonstrated that it’s more proper to use “Evans snow” or “Liu’s sector snow” for snow, and the largest discrepancies come from “soft spheroid” (e.g., Ekelund et al., 2020; Gong et al., 2021). The two snow shapes usually produce quite similar results for sub-mm and MW channels. This is the part that I feel uncomfortable for the settings and suggest to change. For “graupel”, usually we use “8-column aggregates” in ARTS, but I guess that’s not a serious issue as I expect sub-mm and high-frequency MW bands to be saturated to graupels quickly anyway.

I’m not surprised to see graupel retrieval error is not sensitive to mMOD, and agree with the authors that cumulus parameterization scheme is probably that matters for graupel instead of microphysics details in cloud ice and snow (still surprised me that tuning entrainment rate seems to not work). However, it’s also worth noting in the paper that none of these channels are really sensitive to graupel, so it’s expected that the synergy of the three would get things worse. I might have overlooked this point in the manuscript, or I feel it is not emphasized enough in the discussion related to Fig. 8 & Fig. 9.

(4) Another relatively important issue that was overlooked in mRT experiment is the assumption of particle size distribution (PSD), which matters a lot for sub-mm and MW channels. There is a variety of PSD choices in ARTS, and I think it worths to be considered in the mRT experiments.

Minor issues:
Fig. 5, 7 & 9: I’d strongly suggest you to include a mean IWC profile with standard deviation as a reference panel in addition to the current ones. This is helpful for readers to at least visually assess the percentage error of improvements. For example, I found it’s very interesting to see mRT improves cloud ice retrieval when you focus on snow (Fig. 7a, cyan vs. green). By the way, mRT seems to be mis-spelled as “mTR” in all three figure sub-titles.

Section 5.3: Can’t agree you more with your point #3 and #4. For #3, please consider citing Barlakas and Eriksson (2020). It’s a nice paper focusing on sub-grid variability for sub-mm radiometer retrievals. For models with 5 km resolution, it’s comparable to footprint size of these sensors but facing similar order of sub-grid variability. For #4, it is real when it comes to the real algorithm design for combined algorithms, which worths another paper to discuss and #3 and #4 are tightly tied. (guess this is just my comment)

References:
Barlakas and Eriksson (2020): https://doi.org/10.3390/rs12030531
Ekelund et al. (2020): https://doi.org/10.5194/amt-13-501-2020
Gong et al. (2021): https://doi.org/10.5194/essd-13-5369-2021

Citation: https://doi.org/10.5194/egusphere-2023-446-RC2
- AC2: 'Reply on RC2', Ethel Villeneuve, 14 Nov 2023
  
  The authors would like to thank the reviewer for taking the time to make comment on the manuscript. It helped us to clarify it.
  The detailed response is attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-446-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Ethel Villeneuve on behalf of the Authors (11 Jan 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (12 Jan 2024) by Andrew Sayer

RR by Anonymous Referee #2 (10 Mar 2024)

Suggestions for revision or reasons for rejection

Review comments for “synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework” revision version by Villeneuve et al.
Since I’m one of the original reviewers, I’ll skip summary but rather directly move to the conclusion: there are only a very few final touches that I’d like to see the authors to make before final publication. This is a fantastic work that I think deserves highlight.
First, thanks to the authors who adapt my comments/suggestions into their revised version. Now your conclusions regarding their frozen hydrometeor species are much clearer to readers.
Minor suggestions:
Title: add “frozen” before hydrometeor. You didn’t touch upon liquid nor rain.
Fig. 5, 8 and 11: I still think adding a 3rd panel for percentage improvements (standard deviation/weighted mean mass at every pressure level) would be more straightforward.
Fig. 9 and 12: add the three colored lines in the legend to avoid readers going back-and-forth to Fig. 6 to check the meaning of each line.
Line 12: add “more” before accurate.
Line 15: delete “created”.
Line 36: There’s a new publication studying benefits of synergistic radar + radiometer + lidar. No need to cite here, just to make authors aware of. They took different approach because they had field campaign “truth” data to validate. I like you approach here very much when the benchmark “truth” doesn’t exist.
https://doi.org/10.1175/JTECH-D-23-0028.1
Line 65: “the assimilation”: this needs some clarification, I think. This is an OSSE paper, not a DA paper ultimately.
Line 228: what is the “chosen assumption”? Please be clear about it here.
Line 249: You are talking about 37 GHz, but Fig. 3 uses 89 GHz??
Section 4.2: Suggest replacing “CIW” with “SIW”. Same for Section 4.3, replacing “CIW” with “GIW”.
Discussion section: please consider also adding some brief discussions regarding “PSD”. Also, please emphasize again that you didn’t perform individual bias correction for each of your mRT, mMOD and mALL experiments vs. mNOERR has bias correction. This impacts your results, on a minor way I suppose. If you do individual bias correction, the error might get improved.

Hide

ED: Publish subject to technical corrections (11 Mar 2024) by Andrew Sayer

Dear authors,

Thank you for submitting your revised manuscript. I received one further review of this version, and from the review and my own reading, am happy to accept this study for publication in AMT following a few more very minor edits/clarifications. I don't anticipate these needing another round of formal peer review.

We both feel that this is a novel, interesting, and important study.

Minor suggestions:
- Title: add “frozen” before hydrometeor. You didn’t touch upon liquid nor rain.
- Fig. 5, 8 and 11: I still think adding a 3rd panel for percentage improvements (standard deviation/weighted mean mass at every pressure level) would be more straightforward.
- Fig. 9 and 12: add the three colored lines in the legend to avoid readers going back-and-forth to Fig. 6 to check the meaning of each line.
- Line 12: add “more” before accurate.
- Line 15: delete “created”.
- Line 36: There’s a new publication studying benefits of synergistic radar + radiometer + lidar. No need to cite here, just to make authors aware of. They took different approach because they had field campaign “truth” data to validate. I like you approach here very much when the benchmark “truth” doesn’t exist. https://doi.org/10.1175/JTECH-D-23-0028.1
- Line 65: “the assimilation”: this needs some clarification, I think. This is an OSSE paper, not a DA paper ultimately.
- Line 228: what is the “chosen assumption”? Please be clear about it here.
- Line 249: You are talking about 37 GHz, but Fig. 3 uses 89 GHz??
- Section 4.2: Suggest replacing “CIW” with “SIW”. Same for Section 4.3, replacing “CIW” with “GIW”.
- Discussion section: please consider also adding some brief discussions regarding “PSD”. Also, please emphasize again that you didn’t perform individual bias correction for each of your mRT, mMOD and mALL experiments vs. mNOERR has bias correction. This impacts your results, on a minor way I suppose. If you do individual bias correction, the error might get improved.

Please let me know if you have any questions about the above. Best wishes,

Andrew

Hide

AR by Ethel Villeneuve on behalf of the Authors (20 Mar 2024) Author's response Manuscript

Journal article(s) based on this preprint

12 Jun 2024

| Highlight paper

Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié

Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024,https://doi.org/10.5194/amt-17-3567-2024, 2024

Short summary Executive editor

Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié

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Jul 2023	17	7	3	27
Aug 2023	11	11	0	22
Sep 2023	37	8	2	47
Oct 2023	35	13	3	51
Nov 2023	12	6	2	20
Dec 2023	7	8	4	19
Jan 2024	18	3	1	22
Feb 2024	14	12	1	27
Mar 2024	33	13	4	50
Apr 2024	20	4	4	28
May 2024	18	7	1	26
Jun 2024	16	3	0	19

Cumulative views and downloads (calculated since 14 Mar 2023)

Month	HTML	PDF	XML	Total
Mar 2023	65	30	3	98
Apr 2023	51	12	3	66
May 2023	28	10	0	38
Jun 2023	9	3	1	13
Jul 2023	17	7	3	27
Aug 2023	11	11	0	22
Sep 2023	37	8	2	47
Oct 2023	35	13	3	51
Nov 2023	12	6	2	20
Dec 2023	7	8	4	19
Jan 2024	18	3	1	22
Feb 2024	14	12	1	27
Mar 2024	33	13	4	50
Apr 2024	20	4	4	28
May 2024	18	7	1	26
Jun 2024	16	3	0	19

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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.

Preprint (8289 KB)
Metadata XML

This is a very thorough quantification of uncertainties in hydrometeor retrieval from synergistic retrievals. This is rare (and difficult) both in that a thorough uncertainty analysis, and multi-sensor synergy retrievals, are both uncommon - let alone together. This analysis can be a pathfinder for this community and for others seeking to achieve similar goals. Uncertainty analyses are becoming increasingly important as sensors and retrievals improve, and as models are being more sophisticated about use of this information for assimilation or analysis.

Short summary

In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to the top of clouds and microwave sensitive to precipitation. However, infrared satellite observations are underuse. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecast. It shows that the synergistic use of infrared and microwave observations is beneficial, depite inconsistencies.


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