Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

Prange, Marc; Brath, Manfred; Buehler, Stefan A.

doi:https://doi.org/10.5194/amt-14-7025-2021

Articles | Volume 14, issue 11

https://doi.org/10.5194/amt-14-7025-2021

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

https://doi.org/10.5194/amt-14-7025-2021

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

Articles | Volume 14, issue 11

Research article

|

05 Nov 2021

Research article |

| 05 Nov 2021

Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

Marc Prange, Manfred Brath, and Stefan A. Buehler

Download

Final revised paper (published on 05 Nov 2021)
Preprint (discussion started on 06 May 2021)

Interactive discussion

Status: closed

RC1:
'Comment on amt-2021-48', Anonymous Referee #1, 20 May 2021

1. Since the performance of EML retrieval is heavily replying on the additional information from temperature profile, the temperature averaging kernel results should also be shown. Moreover, if temperature retrieval is not performed, instead, reanalysis or forecast temperature profiles are used, will the EML retrieval be improved more?

2. On the additional temperature information, does the retrieval need the detailed vertical structure of temperature or a a smoothed "truth" temperature profile is enough?

Citation: https://doi.org/10.5194/amt-2021-48-RC1
- AC1: 'Reply on RC1', Marc Prange, 01 Jun 2021
  
  Dear Anonymous Referee #1,
  we thank you for your comments. In the following we address each comment individually:
  Referee Question:
  Since the performance of EML retrieval is heavily relying on the additional information from temperature profile, the temperature averaging kernel results should also be shown.
  Answer:
  A key finding is indeed the reliance of the EML retrieval on the added temperature information. Hence, we agree that showing the temperature averaging kernels is beneficial (Figure 1). We think that it is sufficient to add them to the appendix of the paper since they do not appear as essential as the water vapor averaging kernels for the main train of thought in section 4. However, we will make sure to reference the appendix figure in that section.
  Referee Question:
  Moreover, if temperature retrieval is not performed, instead, reanalysis or forecast temperature profiles are used, will the EML retrieval be improved more?
  
  On the additional temperature information, does the retrieval need the detailed vertical structure of temperature or is a smoothed "truth" temperature profile enough?
  Answer:
  We try to answer these two questions simultaneously. In Fig. 4 of the manuscript, we pick a particularly large error in our a priori temperature assumption to qualitatively demonstrate its effect on the water vapor retrieval. In practice, reanalysis or forecast products are expected to be much less biased and to be a somewhat smoothed version of the true temperature profile, similar to what Anonymous Referee #1 suggests in their second question. This indeed denotes another interesting testcase, which we tried to implement with a new retrieval run, the results of which are shown in Figure 2 of the Supplement. Here, the a priori temperature profile is set to be the true profile without the temperature inversion features and the temperature profile retrieval is omitted. The effect on the water vapor retrieval is that the retrieved EML is overly pronounced and in a slightly wrong altitude. Note that the assumed temperature a priori is highly idealized in this example. Forecasts or reanalysis temperature data would be expected to be more error prone. We set up another testcase that only deviates from the previous one by a constant 3 K bias (Figure 3). The result is that the water vapor retrieval does not converge properly (canceled after 20 steps) and errors grow much larger. To avoid having the water vapor retrieval attempt to compensate for temperature errors, it is necessary to simultaneously retrieve the temperature profile. We conclude that missed fine temperature structures deteriorate the EML retrieval but do not yield an EML blindspot, as we also try to convey in the manuscript.
  
  Citation: https://doi.org/10.5194/amt-2021-48-AC1
RC2:
'Comment on amt-2021-48', Anonymous Referee #3, 08 Jun 2021

Review of "Are elevated moist layers a blind spot for hyperspectral infrared sounders? – A model study" by Prange et al.

The authors demonstrated that mid-tropospheric water vapor information is dependent on knowledge about atmosheric temperature. I enjoyed reading the paper but question its basic premise. The fact that water vapor information from hyperpectral sounder measurements is dependent on a-priori knowledge of surface and air temperature is a well established fact.

At first I prepared a detailed review, but I decided to focus this response on my main question to the authors instead: Who is your target audience? I see no reference to any of the excellent papers on IR information content from either the retrieval or data assimilation communities, whether for AIRS, CrIS or IASI. Except for Rodgers, the authors communicate no awareness of any of the operational or research algorithms successfully retrieving mid-tropospheric moisture across the globe on a daily basis. Not to mention the excellent studies on channel selection, error estimation, a-priori selection and least squares fitting. I can provide a long list of references if needed.

Except for responding to Stevens et al. 2017 (which I haven't read, but the authors stated in their motivation throughout), I am not convinced this paper has scientific merit.

Citation: https://doi.org/10.5194/amt-2021-48-RC2
- AC2:
  'Reply on RC2', Marc Prange, 11 Jun 2021
  
  Dear anonymous referee #3,
  we thank you for laying out your major concerns with our paper. In the following, we want to address the raised points individually.
  Referee comment:
  The fact that water vapor information from hyperpectral sounder measurements is dependent on a-priori knowledge of surface and air temperature is a well established fact.
  Response:
  While this appears to be insufficiently communicated in the paper, we are well aware of the fact that we are not introducing a new idea to the field by finding that the water vapor retrieval is reliant on the knowledge of surface and air temperature. We agree with the referee that this generally is a well established fact. However, our finding is not of such general nature, but instead applied to a very specific and to our knowledge novel use case, namely an Elevated Moist Layer (EML). In this context, we believe it is not at all obvious, how the EML retrieval is affected by temperature errors due to the strong temperature inversion features associated with the EML. In the light of the poor retrieval results of Stevens et al. (2017) for an EML case, we view our assessment of the temperature error effect for this specific case as a valuable insight, although in the end it does not appear to be the driving effect for the severely underestimated EML found by Stevens et al. (2017).
  Referee comment:
  I see no reference to any of the excellent papers on IR information content from either the retrieval or data assimilation communities, whether for AIRS, CrIS or IASI. Except for Rodgers, the authors communicate no awareness of any of the operational or research algorithms successfully retrieving mid-tropospheric moisture across the globe on a daily basis. Not to mention the excellent studies on channel selection, error estimation, a-priori selection and least squares fitting.
  Response:
  We do cite IR retrieval literature and use their results as a premise to motivate our study. We cite Schneider and Hase (2011) and Borger et al. (2018) to raise the point that temperature induced errors are among the highest error sources for lower and mid tropospheric water vapor retrievals. We also cite Lacour et al. (2012) as the predecessor study of Stevens et al. (2017) and deploy their spectral setup to better put our results into context. However, we should have made it more clear in the text that there is a wider literature on hyperspectral IR retrieval in general and will amend that in the revised text. For example, we see the point of referencing more performance evaluation studies of operational or research algorithms that derive water vapor profiles from IASI or AIRS, such as Chazette et al. (2014) or Divarkarla et al. (2006). But we want to cite only those papers that are either directly relevant for this paper or important landmarks. If the reviewer thinks that a particular paper in these two categories is missing, then please suggest it.
  Referee comment:
  Who is your target audience?
  Response:
  The aim of our study is not to introduce new insights on retrieval methodology, but rather to investigate to what degree it is possible to retrieve layered moisture features in the troposphere based on established retrieval techniques with IASI. Hence, our target audience is less the retrieval and data assimilation community, but rather the community interested in exploiting satellite data for measurement campaign or climatology purposes with particular focus on the vertical humidity structure. We currently try to convey this in our introduction by mostly referencing literature that puts EMLs into a meteorological context. However, we can see that the current length and somewhat fundamental nature of the retrieval method section may raise unintended expectations for the reader. We could see that a more concise retrieval method section would be sufficient and beneficial for the story we want to tell. Any other suggestions for communicating our target audience clearer are of course welcome.
  Referee Comment:
  Except for responding to Stevens et al. 2017 (which I haven't read, but the authors stated in their motivation throughout), I am not convinced this paper has scientific merit.
  Response:
  With the exception of Calbet et al. (2006), which we will discuss in the revised version of the article, we are not aware of any study other than Stevens et al. (2017) that has addressed the subject of our paper, namely to what extent the retrieval from these instruments is able to faithfully characterise layered moisture structures, in particular EMLs. Typically, examples given in the retrieval literature are for rather smooth profiles, and resolution metrics are theoretical (resulting from OEM analysis), but not put to a practical test (e.g. Lerner et al., 2002). Any suggestion of studies that we might have missed that deal with this would be highly appreciated.
  
  We should more clearly communicate the scientific added value of our finding that EMLs appear possible to retrieve. Rather than focus on pointing out that our results oppose the finding of Stevens et al. (2017), we will further elaborate on the added value of being able to investigate these features in their atmospheric environment based on satellite data.
  References:
  
  Chazette, P., Marnas, F., Totems, J., and Shang, X.: Comparison of IASI water vapor retrieval with H₂O-Raman lidar in the framework of the Mediterranean HyMeX and ChArMEx programs, Atmos. Chem. Phys., 14, 9583–9596, https://doi.org/10.5194/acp-14-9583-2014, 2014.
  Divakarla, M. G., Barnet, C. D., Goldberg, M. D., McMillin, L. M., Maddy, E., Wolf, W., Zhou, L., and Liu, X. (2006), Validation of Atmospheric Infrared Sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts, J. Geoph. Res., 111, D09S15, doi:10.1029/2005JD006116.
  Lerner, J. A., Weisz, E., and Kirchengast, G., Temperature and humidity retrieval from simulated Infrared Atmospheric Sounding Interferometer (IASI) measurements, J. Geoph. Res., 107( D14), doi:10.1029/2001JD900254, 2002.
  Calbet, X., Schluessel, P., Hultberg, T., Phillips, P., August, T., (2006). Validation of the operational IASI level 2 processor using AIRS and ECMWF data. Advances in Space Research. 37. 2299-2305. 10.1016/j.asr.2005.07.057.
  
  Citation: https://doi.org/10.5194/amt-2021-48-AC2
  - RC3: 'Reply on AC2', Anonymous Referee #3, 23 Jun 2021
    
    Thanks to the authors for clarifying. I reread the paper with their response in mind and acknowledge that there is value in studying the information content and retrievability of target weather/climate features. Specifically, the authors focus on demonstrating the observing capability of passive infrared sounders with respect to elevated moist layers (EMLs). They do this using simulated IASI radiances of model atmospheres over ocean and in clear skies.
    The authors presented their work clearly and accurately. Their paper reads well and has a logical flow. My primary concern is with the scientific value of their work. Their findings are not new, their experimental set up is naive, their test case(s) simplistic and they fail to recognize the work by others on hyperspectral infrared sounders from the past four decades. IASI has been in orbit since 2006. Its predecessor, AIRS, was launched in 2002 and both instruments have since seen two CrIS instruments join them in low Earth orbit. At the turn of the century, these hyperspectral infrared sounders revolutionized space-based vertical atmospheric observations. Now, we have nearly two decades of real measurements publicly available as a scientific community and well documented retrieval products from multiple different algorithms with which to study weather and climate phenomena. The existing record of retrieval products and the algorithms they are based on is by no means perfect or complete, but I fail to see how the work presented in this paper contributes to this body of knowledge.
    General notes:
    Hyperspectral infrared sounders have hundreds of channels that allow one to apply sophisticated channel selection methods (e.g., Gambacorta and Barnet, 2011; Coopmann et al., 2020; Rabier et al., 2002; Fourrié and Rabier, 2004; Fourrié and Thépaut, 2003; Engelen and Bauer, 2014; Collard, 2007; Ventress and Dudhia, 2014; Martinet et al., 2014; Chang et al., 2020) to stabilize and maximize information content for a target variable. As reader and reviewer, I think Section 2.1 can be strengthened with a short description of the main principles of the method they employed, followed by a justification for their total of 1845 channels. That is a lot of channels. Why did the authors not thin it down, given the large degree of redundant information in these channels?
    As far as different retrieval methods go, there are many excellent examples. I am listing here a few that focus on water vapor profiles (Smith and Weisz, 2018; Smith and Barnet, 2020; Smith et al., 2012, 2015; Susskind et al., 2003, 2014; Smith and Barnet, 2019; DeSouza-Machado et al., 2018; Irion et al., 2018; Weisz et al., 2013; Maddy et al., 2009).
    An interesting aspect of this study is the author’s choice of using temperature channels from the shortwave IASI band. This is an unusual choice because, historically, radiative transfer models generated large biases for the shortwave channels due to non-LTE effects (Yin, 2016; DeSouza-Machado et al., 2007) that cause diurnal differences. This is mostly addressed in modern-era radiative transfer models (e.g., SARTA and RTTOV) so this effect is minimized so that data assimilation and retrieval teams are looking at the shortwave temperature channels anew. Do the authors see diurnal differences in their results? It will be interesting if the authors can repeat their study but with temperature channels from the longwave IASI band as comparison.
    Can the authors include a paragraph in their Summary section stating their thoughts on the value of their results to future algorithm upgrades or new instruments, like IASI-NG? Here are examples of how National Weather Service forecasters in the USA use NUCAPS retrievals (NOAA-Unique Combined Atmospheric Processing System) (Esmaili et al., 2020; Berndt et al., 2020), and the value they find in mid-tropospheric moisture retrievals. I wonder if the authors have observed EMLs in any one of the operational, publicly available retrieval products from NUCAPS (CrIS and IASI), CLIMCAPS (AIRS and CrIS), AIRS V7 or the EUMETSAT IASI Level 2 products? Do these products fail to sufficiently capture the EMLs in question?
    Specific notes:
    Line 57: Can the authors give examples of what they mean by “instrument issues”? Clouds would be another factor.
    Line 72: Instead of simply stating “poses an inverse problem”, I suggest preemptively qualifying this statement as “poses an under-constrained inverse problem”.
    Line 80: There are many examples in the literature of research and operational retrieval algorithms that employ OEM for hyperspectral IR sounders, namely AIRS, CrIS and IASI. To strengthen this statement and communicate awareness of these other systems, I recommend that the authors add citations to these other OEM algorithms.
    Line 88: For those unfamiliar with the channel selection method of Schneider and Hase (2011), I recommend that the authors add a sentence or two explaining the basic premise. There are numerous approaches to selecting hyperspectral IR channels and I think a clarification and justification of the authors’ choice will strengthen this work.
    Line 90: The spectral signal of water vapor is sensitive to temperature, yes, but also mid-tropospheric methane, surface emissivity and temperature and nitrous oxide (to a lesser degree).
    Lines 95-96: Can the authors substantiate this statement with a citation?
    Line 97: I’m intrigued by the authors’ choice of using shortwave CO2 channels for retrieving temperature information. This is an unconventional choice as most operational retrieval and data assimilation algorithms employ longwave CO2 channels for temperature information. Can the authors justify their choice and discuss the benefits of using these shortwave CO2 channels?
    Figure 1: I’m wondering if I read this figure correctly. Does each IASI channel in the range 1250 – 2000 cm-1 have 1 x degree of freedom (DOF)? This appears too high. Can the authors explain how they defined their variables for the Rodgers (2000) DOF equation?
    Lines 99-102: I return again to the question about the channel selection method employed. Does the Boukachaba et al. (2015) method use the same principles as Schneider and Hase (2011)? A total of 1845 channels. How many of these channels are used for water vapor information? What are the exact spectral ranges where these channels come from? How many DOF does this set of channels have for water vapor versus surface and air temperature? I’m wondering how much spectral redundancy the authors are factoring into their method and why.
    Line 141: This is the first time I read of ARTS. For IASI radiative transfer calculations, I’m much more familiar with RTTOV. From reading this section, I conclude that the authors used ARTS for its “internal OEM module” (Line 155), and not for its more accurate shortwave radiative transfer calculations. Is this correct? And are the authors confident that ARTS calculate non-LTE effects for IASI shortwave IR channels correctly? What vertical grid does ARTS employ?
    Line 207: “making the term EML more graspable”… What does this mean?
    Line 209: “EMLs can be described as layers of anomalously large humidity…”. This is a very awkward statement that I struggle to understand.
    Line 210: “one unconsciously also envisions”? I suggest rewriting this paragraph. It is difficult to follow.
    Paragraph starting on line 215: I fully agree with the authors statement and appreciate their clear explanation here.
    Line 234: As made clear from the beginning, the study the authors present here is in response to the findings by Stevens et al. (2017). I see the value in using the same atmospheric test case and this makes me wonder if Stevens et al. (2017) also used ARTS? The results the authors present here draws a different conclusion, but how much of that is due to differences in experimental set up, e.g., choice of radiative transfer model, channel subsets, simultaneous retrieval of air and surface temperature, etc. I’m curious to know how the water vapor averaging kernels compare between your study and that of Stevens et al. (2017). Did the authors achieve a similar signal-to-noise?
    Line 271: This statement starting with “Note that…” should be introduced and explained early on in the text to avoid creating the confusion that I now find myself in as reader and reviewer.
    Line 281: “. It shows that the EML strength s_anom of retrieval setup 1 is about half the value of the true state”. What does this mean?
    Table 2: I assume the authors converted profiles in pressure units to distance units using the geopotential height calculation? It will be helpful if the caption explain what “Strength” means. Of the four quantities reported here, “Strength” is the most obscure and abstract, being without units.
    Lines 287-299: I appreciate the authors’ clear, direct response to Stevens et al. (2017), refuting their notion that hyperspectral IR measurements lack mid-tropospheric water vapor information. I agree with the authors’ main conclusions here, but as stated earlier, it is well established that the retrieval of water vapor information depends on knowledge about temperature. I suggest that the authors acknowledge this with relevant citations. E.g., as far as retrieval systems go, there is the simultaneous approach (Smith et al., 2012; Weisz et al., 2013; Irion et al., 2018) and sequential approach (Smith and Barnet, 2020, 2019; Susskind et al., 2014, 2003; Maddy et al., 2009) to account for temperature uncertainty in water vapor retrievals.
    Line 316: “Values close to 1 indicate a good sensitivity of the retrieval.” This sentence is misleading since it appears to refer to the black line in Fig. 5(a) and (c). The correct statement should be: Averaging kernel values close to 1 indicate strong sensitivity of the retrieval to the true state. But because the inversion of hyperspectral IR measurements into water vapor profiles is, by definition, an under constrained, ill-posed solution, averaging kernels never approach 1 (see Smith and Barnet, 2020 and references therein).
    While accurate, this discussion lacks depth without citations and only a single example of “an exemplary” EML case. I wonder how the water vapor averaging kernels for EML’s change under different temperature conditions, such as day versus night. Can the authors include a sentence on the sensitivity of averaging kernels to different EML cases?
    References
    Berndt, E. B., Smith, N., Burks, J., White, K., Esmaili, R., Kuciauskas, A., Duran, E., Allen, R., LaFontaine, F., and Szkodzinski, J.: Gridded Satellite Sounding Retrievals in Operational Weather Forecasting: Product Description and Emerging Applications, 12, 3311, https://doi.org/10.3390/rs12203311, 2020.
    Chang, S., Sheng, Z., Du, H., Ge, W., and Zhang, W.: A channel selection method for hyperspectral atmospheric infrared sounders based on layering, 13, 629–644, https://doi.org/10.5194/amt-13-629-2020, 2020.
    Collard, A. D.: Selection of IASI channels for use in numerical weather prediction, 133, 1977–1991, https://doi.org/10.1002/qj.178, 2007.
    Coopmann, O., Guidard, V., Fourrié, N., Josse, B., and Marécal, V.: Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP), 13, 2659–2680, https://doi.org/10.5194/amt-13-2659-2020, 2020.
    DeSouza-Machado, S., Strow, L. L., Tangborn, A., Huang, X., Chen, X., Liu, X., Wu, W., and Yang, Q.: Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm, 11, 529–550, https://doi.org/10.5194/amt-11-529-2018, 2018.
    DeSouza-Machado, S. G., Strow, L. L., Hannon, S. E., Motteler, H. E., Lopez-Puertas, M., Funke, B., and Edwards, D. P.: Fast forward radiative transfer modeling of 4.3 μm nonlocal thermodynamic equilibrium effects for infrared temperature sounders, 34, https://doi.org/10.1029/2006GL026684, 2007.
    Engelen, R. J. and Bauer, P.: The use of variable CO2 in the data assimilation of AIRS and IASI radiances, 140, 958–965, https://doi.org/10.1002/qj.919, 2014.
    Esmaili, R. B., Smith, N., Berndt, E. B., Dostalek, J. F., Kahn, B. H., White, K., Barnet, C. D., Sjoberg, W., and Goldberg, M.: Adapting satellite soundings for operational forecasting within the hazardous weather testbed, 12, 886, https://doi.org/10.3390/rs12050886, 2020.
    Fourrié, N. and Rabier, F.: Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas, 130, 1839–1856, https://doi.org/10.1256/qj.03.27, 2004.
    Fourrié, N. and Thépaut, J.-N.: Evaluation of the AIRS near-real-time channel selection for application to numerical weather prediction, 129, 2425–2439, https://doi.org/10.1256/qj.02.210, 2003.
    Gambacorta, A. and Barnet, C. D.: Methodology and information content of the NOAA NESDIS operational channel selection for the Cross-Track Infrared Sounder (CrIS), 2011.
    Irion, F. W., Kahn, B. H., Schreier, M. J., Fetzer, E. J., Fishbein, E., Fu, D., Kalmus, P., Wilson, R. C., Wong, S., and Yue, Q.: Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS, 11, 971–995, https://doi.org/10.5194/amt-11-971-2018, 2018.
    Maddy, E. S., Barnet, C. D., and Gambacorta, A.: A computationally efficient retrieval algorithm for hyperspectral sounders incorporating a-priori information, 6, 802–806, https://doi.org/10.1109/LGRS.2009.2025780, 2009.
    Martinet, P., Lavanant, L., Fourrié, N., Rabier, F., and Gambacorta, A.: Evaluation of a revised IASI channel selection for cloudy retrievals with a focus on the Mediterranean basin, 140, 1563–1577, https://doi.org/10.1002/qj.2239, 2014.
    Rabier, F., Fourrié, N., Chafaï, D., and Prunet, P.: Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances, 128, 1011–1027, https://doi.org/10.1256/0035900021643638, 2002.
    Smith, N. and Barnet, C. D.: Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS), 11, 1227, https://doi.org/10.3390/rs11101227, 2019.
    Smith, N. and Barnet, C. D.: CLIMCAPS observing capability for temperature, moisture, and trace gases from AIRS/AMSU and CrIS/ATMS, 13, 4437–4459, https://doi.org/10.5194/amt-13-4437-2020, 2020.
    Smith, N., Smith, W. L., Weisz, E., and Revercomb, H. E.: AIRS, IASI and CrIS retrieval records at climate scales: An investigation into the propagation systematic uncertainty, JAMC, 54, 1465–1481, 2015.
    Smith, W. L. and Weisz, E.: Dual-regression approach for high-spatial-resolution infrared soundings, 7, 297–311, https://doi.org/10.1016/B978-0-12-409548-9.10394-X, 2018.
    Smith, W. L., Weisz, E., Kireev, S. V., Zhou, D. K., Li, Z., and Borbas, E. E.: Dual-regression retrieval algorithm for real-time processing of satellite ultraspectral radiances, JAMC, 51, 1455–1476, https://doi.org/10.1175/JAMC-D-11-0173.1, 2012.
    Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds, IEEE TGRS, 41, 390–409, 2003.
    Susskind, J., Blaisdell, J. M., and Iredell, L.: Improved methodology for surface and atmospheric soundings, error estimates, and quality control procedures: the atmospheric infrared sounder science team version-6 retrieval algorithm, 8, 084994, https://doi.org/10.1117/1.JRS.8.084994, 2014.
    Ventress, L. and Dudhia, A.: Improving the selection of IASI channels for use in numerical weather prediction, 140, 2111–2118, https://doi.org/10.1002/qj.2280, 2014.
    Weisz, E., Smith, W. L., and Smith, N.: Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high-spectral resolution radiance measurements, J. Geophys. Res. Atmos., 118, 6433–6443, https://doi.org/10.1002/jgrd.50521, 2013.
    Yin, M.: Bias characterization of CrIS shortwave temperature sounding channels using fast NLTE model and GFS forecast field, 121, 1248–1263, https://doi.org/10.1002/2015JD023876, 2016.
    
    Citation: https://doi.org/10.5194/amt-2021-48-RC3
    
    AC3: 'Reply on RC3', Marc Prange, 30 Jun 2021
    
    The authors thank the referee for their comprehensive and constructive comments. We want to limit this initial response to convey our general notion regarding the referee’s primary concern, namely the scientific value of our study.
    The referee expresses their struggle in trying to see added scientific value in our study with respect to the “body of knowledge” in hyperspectral remote sensing established over the past decades. We view this point as closely coupled to the referee’s initial question, “Who is your target audience?” (https://doi.org/10.5194/amt-2021-48-RC2). As pointed out in our initial response, our target audience is less the retrieval and data assimilation community, to whom we agree, the deployed retrieval methods and analysis tools barely offer new insights. Also, the general idea of a temperature dependence of the humidity retrieval is nothing new as we elaborated in our previous response. We plan to have our manuscript better reflect what exactly is established knowledge and where we contribute new ideas and analysis, which is with respect to the specific use case of EMLs. To do so, we will try to be more explicit about this in the text and expand our literature list by some of the studies proposed by the reviewer and . Our goal is to make it obvious for the reader that the focal point of our study is the question of how well moisture anomalies in the mid troposphere can be captured by IASI – given a quite standard physical retrieval scheme – and not on advancing retrieval methods.
    While the discussion phase is now ending, we hope that with a revised version of the manuscript we will be able to find common ground with the referee regarding the scientific value of our study, which of course is very important to us. We look forward to improving our manuscript and thank the reviewer for their constructive comments that will significantly contribute to that.
    
    Citation: https://doi.org/10.5194/amt-2021-48-AC3

Peer review completion

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

AR by Marc Prange on behalf of the Authors (20 Aug 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (29 Aug 2021) by Brian Kahn

RR by Anonymous Referee #3 (14 Sep 2021)

RR by Anonymous Referee #1 (27 Sep 2021)

ED: Publish as is (27 Sep 2021) by Brian Kahn

AR by Marc Prange on behalf of the Authors (30 Sep 2021)

Short summary

We investigate the ability of the hyperspectral infrared satellite instrument IASI to resolve moist layers in the tropical free troposphere in a model framework. Previous observational results indicated major deficiencies of passive satellite instruments in resolving moist layers around the freezing level. We conduct a first systematic hyperspectral infrared retrieval analysis of such moist layers and conclude that no inherent satellite blind spot for moist layers exists.