Assessing the potential of free tropospheric water vapour isotopologue satellite observations for improving the analyses of latent heating events

Schneider, Matthias; Toride, Kinya; Khosrawi, Farahnaz; Hase, Frank; Ertl, Benjamin; Diekmann, Christopher Johannes; Yoshimura, Kei

doi:https://doi.org/10.5194/egusphere-2023-1121

Preprints

https://doi.org/10.5194/egusphere-2023-1121

Preprints

08 Nov 2023

| 08 Nov 2023

Assessing the potential of free tropospheric water vapour isotopologue satellite observations for improving the analyses of latent heating events

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher Johannes Diekmann, and Kei Yoshimura

Abstract. Satellite-based observations of free tropospheric water vapour isotopologue ratios (δD) with good global and temporal coverage have become recently available. We investigate the potential of these observations for constraining the uncertainties of the atmospheric analyses ﬁelds of speciﬁc humidity (q), temperature (T), and δD and of variables that capture important properties of the atmospheric water cycle, namely the vertical velocity (ω), the latent heating rate (Q₂), and the precipitation rate (Prcp). Our focus is on the impact of the δD observations if used in addition to the observation of q and T , which are much easier to be observed by satellites and routinely in use for atmospheric analyses. For our investigations we use an Observing System Simulation Experiment, i.e. simulate the satellite observations of q, T , and δD with known uncertainties, then use them within a Kalman ﬁlter based assimilation framework in order to evaluate their potential for improving the quality of atmospheric analyses. The study is made for low latitudes (30° S to 30° N) and for 40 days between mid-July and end of August 2016. We ﬁnd that the assimilation of q and T observations alone well constrains the atmospheric q and T ﬁelds (analyses skills in the free troposphere of up to 60 %), and moderately constrains the ﬁelds of δD, ω, Q₂, and Prcp (analyses skills of 20 %–40 %). The additional assimilation of δD observations further improves the quality of the analyses of all variables. We use Q₂ as proxy for the presence of condensation and evaporation processes, and we show that the additional improvement is rather weak when evaporation or condensation are negligible (additional analyses skills of generally below 5 %), and strongest for high condensation rates (additional skills of about 15 % and above). The very high condensation rates (identiﬁed by large positive Q₂ values) are rare, but related to extreme events (very high ω and Prcp) that are not well captured in the analyses (for these extreme events also the analyses uncertainties of ω, Q₂, and Prcp are very large), i.e. the additional assimilation of δD observations signiﬁcantly improves the analyses of the water cycle related variables for the events when an improvement is most important. In real world satellite datasets δD observations affected by such strong latent heating events are frequently available, suggesting that the here demonstrated additional δD impact for the simulated world is also a realistic scenario for a real world data assimilation.

Received: 26 May 2023 – Discussion started: 08 Nov 2023

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Journal article(s) based on this preprint

06 Sep 2024

Assessing the potential of free-tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher J. Diekmann, and Kei Yoshimura

Atmos. Meas. Tech., 17, 5243–5259, https://doi.org/10.5194/amt-17-5243-2024,https://doi.org/10.5194/amt-17-5243-2024, 2024

Short summary

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher Johannes Diekmann, and Kei Yoshimura

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1121', Anonymous Referee #1, 29 Nov 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1121/egusphere-2023-1121-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-1121-RC1
- AC1: 'Reply on RC1', Matthias Schneider, 15 Apr 2024
  
  We would like to thank the referee for their help and comments. Please find our replies in the attached pdf file.
  Best regards, Matthias Schneider
  
  Citation: https://doi.org/10.5194/egusphere-2023-1121-AC1
RC2:
'Comment on egusphere-2023-1121', Anonymous Referee #2, 11 Dec 2023

The manuscript evaluates the use of assimilating dD measured by the satellite IASI in addition to traditional meteorological variables like q and T (also measured by IASI) in an Observation System Simulation Experiment (OSSE) with the isotope-enabled climate model IsoGSM. In general, dD adds little but notable skill to the analysis. Since dD carries information about the phase change history of air masses, the assimilation of dD particularly improves the analysis in cases of strong condensation or evaporation (identified by high latent heating/cooling rates). These extreme events usually involve strong precipitation and are therefore societally relevant, but are poorly captured in most analyses. Therefore the improvement for these events is very promising.
The manuscript is well-written and the figures are appropriate. I appreciate that everything is nicely structured and carefully documented. It is easy to follow the authors‘ explanations throughout the manuscript. I have a few comments that can hopefully further improve the manuscript and/or clarify some aspects.

General comments
1) As far as I know IASI does not see through clouds. Were observations in cloudy conditions filtered out before the assimilation? I would expect that strong latent heating events are almost always associated with clouds. If IASI cannot measure dD in these cases, is the improvement of the analysis thanks to dD still realistic?
2) The latent heating rate is defined as the change of specific humidity in an air parcel (material derivative of q) times the latent heat of net condensation (equation 9). However, mixing processes can also lead to a change in specific humidity in air parcels and could therefore bias the analysis. Is there a way to separate mixing from latent heating/cooling, e.g. by diagnosing Q2 directly in the model?
3) I think it might be nice to show also the horizontal spatial distribution (i.e. maps) of the differences in the skills to see the regions where the assimilation does or does not work well. Or is it pretty uniform?
4) Is there a reason why you use daily mean values for the analyses? I would expect that especially the strong latent heating/cooling events are rather short-lived and the improvement could be better on shorter time scales, e.g. 6 hours.

Specific comments
L24: Suggestion: „where latent heat is released or consumed“
L25: impacting on > impacting
L39: As far as I know, it should be D and H in the equation (instead of HD16O and H216O).
L50: There is also an isotope version of NICAM (Tanoue et al., 2023), which might be worth adding here.
L53: clouds or precipitation involving processes > processes involving clouds or precipitation
L83: such assimilation > such an assimilation
L108: What is different in the 96 initializations? Later you write the initial conditions. What exactly is different in the initial conditions?
L155+: Add/Explain somewhere what is a good skill and what is a bad skill? E.g. 100% means perfect, 0% means same bad as the reference.
L157 do do > to do
L162+: This has been said many times already. I think it could be removed here or somewhere else.
L171: „Therefore…“: I don‘t see how this sentence follows from the previous sentence.
L179: … and 17 vertical levels?
L208: 2x between
L257: close the > close to
L281: uncertainty > error
L292: this uncertainties > these uncertainties
L303: Remove „an“
L304: independent on > independent of
L306: quantitatively document > quantify?
L309: How did you define these bins? Why not the same 60 bins as before?
L343: in particularly > in particular / particularly
L346: by the additional assimilating of > by additionally assimilating / by the additional assimilation of
L359: These subset > This subset
L392: I would add NICAM here again.
Figure 1, caption: remove „skill“ after (e). Is „only q“ not also „only one type of observation“ (like „only T“ or „only dD“)?

Reference
Tanoue, M., Yashiro, H., Takano, Y., Yoshimura, K., Kodama, C., & Satoh, M. (2023). Modeling Water Isotopes Using a Global Non‐Hydrostatic Model With an Explicit Convection: Comparison With Gridded Data Sets and Site Observations. Journal of Geophysical Research: Atmospheres, 128(23), e2021JD036419.

Citation: https://doi.org/10.5194/egusphere-2023-1121-RC2
- AC2: 'Reply on RC2', Matthias Schneider, 15 Apr 2024
  
  We would like to thank the referee for their help and comments. Please find our replies in the attached pdf file.
  Best regards, Matthias Schneider
  
  Citation: https://doi.org/10.5194/egusphere-2023-1121-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1121', Anonymous Referee #1, 29 Nov 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1121/egusphere-2023-1121-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-1121-RC1
- AC1: 'Reply on RC1', Matthias Schneider, 15 Apr 2024
  
  We would like to thank the referee for their help and comments. Please find our replies in the attached pdf file.
  Best regards, Matthias Schneider
  
  Citation: https://doi.org/10.5194/egusphere-2023-1121-AC1
RC2:
'Comment on egusphere-2023-1121', Anonymous Referee #2, 11 Dec 2023

The manuscript evaluates the use of assimilating dD measured by the satellite IASI in addition to traditional meteorological variables like q and T (also measured by IASI) in an Observation System Simulation Experiment (OSSE) with the isotope-enabled climate model IsoGSM. In general, dD adds little but notable skill to the analysis. Since dD carries information about the phase change history of air masses, the assimilation of dD particularly improves the analysis in cases of strong condensation or evaporation (identified by high latent heating/cooling rates). These extreme events usually involve strong precipitation and are therefore societally relevant, but are poorly captured in most analyses. Therefore the improvement for these events is very promising.
The manuscript is well-written and the figures are appropriate. I appreciate that everything is nicely structured and carefully documented. It is easy to follow the authors‘ explanations throughout the manuscript. I have a few comments that can hopefully further improve the manuscript and/or clarify some aspects.

General comments
1) As far as I know IASI does not see through clouds. Were observations in cloudy conditions filtered out before the assimilation? I would expect that strong latent heating events are almost always associated with clouds. If IASI cannot measure dD in these cases, is the improvement of the analysis thanks to dD still realistic?
2) The latent heating rate is defined as the change of specific humidity in an air parcel (material derivative of q) times the latent heat of net condensation (equation 9). However, mixing processes can also lead to a change in specific humidity in air parcels and could therefore bias the analysis. Is there a way to separate mixing from latent heating/cooling, e.g. by diagnosing Q2 directly in the model?
3) I think it might be nice to show also the horizontal spatial distribution (i.e. maps) of the differences in the skills to see the regions where the assimilation does or does not work well. Or is it pretty uniform?
4) Is there a reason why you use daily mean values for the analyses? I would expect that especially the strong latent heating/cooling events are rather short-lived and the improvement could be better on shorter time scales, e.g. 6 hours.

Specific comments
L24: Suggestion: „where latent heat is released or consumed“
L25: impacting on > impacting
L39: As far as I know, it should be D and H in the equation (instead of HD16O and H216O).
L50: There is also an isotope version of NICAM (Tanoue et al., 2023), which might be worth adding here.
L53: clouds or precipitation involving processes > processes involving clouds or precipitation
L83: such assimilation > such an assimilation
L108: What is different in the 96 initializations? Later you write the initial conditions. What exactly is different in the initial conditions?
L155+: Add/Explain somewhere what is a good skill and what is a bad skill? E.g. 100% means perfect, 0% means same bad as the reference.
L157 do do > to do
L162+: This has been said many times already. I think it could be removed here or somewhere else.
L171: „Therefore…“: I don‘t see how this sentence follows from the previous sentence.
L179: … and 17 vertical levels?
L208: 2x between
L257: close the > close to
L281: uncertainty > error
L292: this uncertainties > these uncertainties
L303: Remove „an“
L304: independent on > independent of
L306: quantitatively document > quantify?
L309: How did you define these bins? Why not the same 60 bins as before?
L343: in particularly > in particular / particularly
L346: by the additional assimilating of > by additionally assimilating / by the additional assimilation of
L359: These subset > This subset
L392: I would add NICAM here again.
Figure 1, caption: remove „skill“ after (e). Is „only q“ not also „only one type of observation“ (like „only T“ or „only dD“)?

Reference
Tanoue, M., Yashiro, H., Takano, Y., Yoshimura, K., Kodama, C., & Satoh, M. (2023). Modeling Water Isotopes Using a Global Non‐Hydrostatic Model With an Explicit Convection: Comparison With Gridded Data Sets and Site Observations. Journal of Geophysical Research: Atmospheres, 128(23), e2021JD036419.

Citation: https://doi.org/10.5194/egusphere-2023-1121-RC2
- AC2: 'Reply on RC2', Matthias Schneider, 15 Apr 2024
  
  We would like to thank the referee for their help and comments. Please find our replies in the attached pdf file.
  Best regards, Matthias Schneider
  
  Citation: https://doi.org/10.5194/egusphere-2023-1121-AC2

Peer review completion

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

AR by Matthias Schneider on behalf of the Authors (15 Apr 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (17 Apr 2024) by Christof Janssen

RR by Anonymous Referee #1 (17 Apr 2024)

RR by Anonymous Referee #2 (12 May 2024)

Suggestions for revision or reasons for rejection

Thanks to the authors for the revised version of the manuscript. With the additional restriction to cloud-free conditions the results are much more robust and applicable to real-world conditions. The text reads well and the figures are nice and understandable. I only have a few remaining comments.

General comments

1) To clarify my comment 2 on the first version of the manuscript: By mixing I meant turbulent mixing (and diffusion). Equation 9 considers only large-scale advection of q. For example, if the wind velocities at a grid point are 0, and there is a change in specific humidity, it will now be diagnosed as latent heating/cooling, but the change could also be due to turbulent mixing or diffusion. I was wondering whether it would be possible to distinguish this by taking the latent heating rate directly from the model (if it is possible to write it out). If not, maybe at least write that Q2 includes changes in specific humidity due to other processes, such as turbulent mixing or diffusion.

2) The differences between the ensemble mean and the nature run are referred to as uncertainties. When I hear uncertainty I think of something that is always positive, while error can be negative or positive. I would therefore find error a more appropriate term here, but I‘m fine if the authors prefer uncertainty.

Specific comments

L16: I find this sentence confusing. I would rephrase it, e.g.: „The fact that no satellite observations are available at the location and time of the unstable conditions indicates a remote impact of dD observations that are available elsewhere.“

L23: drive the atmospheric circulation („the“ missing).

L38: In the previous review I meant that the ratio is defined as the concentration of the D isotope with respect to the H isotope (not the isotopologues, and same for 18O). See e.g. Coplen (1994). This is not (exactly) the same because the H isotope appears also in H218O for example.

L41: We already know that dD is of particular interest (from L35). I would reformulate to: „The reason why dD is of (particular) interest is ...“

L97: these simulations -> this simulation

L150: See general comment 2.

L170: Suggestion: which is … -> where a more negative value corresponds to a stronger observation impact.

L227: Maybe explain what „significant“ means, like you do on L250.

L245: loose -> lose

L294: the analyses uncertainties of Δqi,j?

L301: „seems to be“ sounds very uncertain. I would change it to „is also slightly larger“

L305: I would change ω value to absolute value of ω or magnitude of ω because it can be positive or negative (same for the error actually).

L307: high -> low? And L308: highest -> lowest?

L374: Maybe briefly explain what a Rayleigh line is for readers who see this for the first time, or add a reference?

L437: Space between observations and are.

L442: Two times „does“. I would suggest: Assimilating dD on top of q and T strongly improves the analysis of dD even further.

L447: Suggestion: as a proxy for atmospheric stability.

L453: I would write: Since unstable atmospheric conditions are almost always associated with clouds, we assimilate no observations at the location and time of these conditions.

L456: For consistency, write HD16O.

Figure 4: Are Q2 and Prcp still per day, or per 6h?

Figure 7: Make the naming consistent (e.g. green line or green, ; or .).

Reference

Coplen, T. B. (1994). Reporting of stable hydrogen, carbon, and oxygen isotopic abundances (technical report). Pure and applied chemistry, 66(2), 273-276.

Hide

ED: Publish subject to minor revisions (review by editor) (11 Jun 2024) by Christof Janssen

Dear Authors,

I am very sorry for the very long delays, but I am glad that your manuscript has now recieved excellent reports from two independent referees. Based on these reports, I am happy to decide that the manuscript is accepted with minor revisions.

In making your revisions, please take into account the recommendations of the two reports. There are three more points that need to be considered in your revision. First, δD is no isotopologue ratio, but a δ-value. You should introduce it as such and use the correct denomination. Then, it is not clear whether this is discussed in the cited literature, but what is surprisingly missing in the introduction of the current manuscript is a short (physical) explanation why δD is/should be an interesting tracer for convective vertical transport, ie beyond your phrase that " δD is strongly affected by vertical transport and convective processes" in line 43 ... The physics is only mentioned much later (section 4) and explicitly referred to when super-Rayleigh events are mentioned. Finally, the "Nature" variable seems to be ill defined. As used in Figs 4 and 6, it is a quantity that depends on ω. Given the units, however, it is a vertical velocity (ω) with "nature" being a qualifier. So you should label the axis ω_nature with the common units of a vertical velocity. Do I understand this correctly ? In this case, Nature(ω) should be replaced by ω_nature (or whatever) throughout the manuscript.

In what follows are some more suggestions that mostly aim at simplifying and improving the reading or at correcting typos.

l25 societal impact -> societal consequences
l25 Because observations of dD at the location and time of these unstable atmospheric conditions are unavailable, this indicates a unique remote impact of elsewhere available δD observations on the analyses of these convective events. you might considering to replace 'remote impact' by '(long-)distance effect' or 'effect at a distance'
l33 determines -> determine
l36 modify -> modifies
l40 realted-> related
l41 check whether it is not preferrable to delete 'the analysed'
l42 assimilaton -> assimilation
l44 this spectrally resolved radiation -> this radiation spectrally resolved
l46 repetitive use of provide, please change
l51 remove the reference to Craig. It does not give the value of V-SMOW.
l119 analyses' ?
Caption table 3: "what" -> "which" , "referring the" -> referring to the
Table 3 Skill calculation should be replaced by Definition of skill
Table 3, see comment to l159.
l159 The definition of skill is a little bit surprising as it is the negative of a relative deviation with respect to the reference. Maybe you should write it a such :
skill = - (exp-ref)/ref. It is evident that you want it to be positive when the relative deviation diminishes. Interestingly, you later use 'loss of skill' which is the negative of the skill and corresponds to a relative deviation.
l161 "informs on the relative change of the RMSD value obtained from an assimilation experiment with respect to the reference assimilation experiment". As mentioned just before, it is the negative of the relative change with respect to the reference.
l163 'of' is missing
l181,184 etc. Consistency with figure caption 1: 'cumulative distribution functions' should be abbreviated either "cdf" or "cdfs" throughout. It is preferrable to use cdf for the singular case and cdfs for the plural case.
l185 For q all -> For q, all; shifter -> shifted
l196 Because for this calculation, continuous time series are used -> Because continuous time series are used for this calculation,
l202 Skill -> Skills
l215 Sect.4. -> Sect. 4.
l235 observation of q, T, and δD -> observations of q, T, and δD
l286 these large amount -> this large amount
l289 until we consider 98% of all the ∆i,j values occurring for the considered ω-bin -> until we consider 98% of all the ∆i,j values occurring for a particular ω-bin
Fig 4. Please indicate quickly in the text (maybe around l285) why downward velocities (0.5 Pa/s) are higher than upward velocities (-1.5 Pa/s) ...
Fig 5. use 10^3, ... 10^9 on the coordinate scale instead of 1000, 100000 ... 1000000000.
l306 This reduction of the ω uncertainty is more pronounced for the ∆{q,T,δD} than for the ∆{q,T} distributions -> This reduction of the ω uncertainty is slightly more pronounced for the ∆{q,T,δD} than for the ∆{q,T} distributions
l307 and largest for high actual ω values. -> and largest for high absolute values of ω.
l324 together 441253 events, which is 441253/586800 = 75.20% -> together 441253 out of 586800 events, which is 75.2%.
l325 Nature -> nature
l325 only comprises 5363 events, i.e. only 5363/586800 = 0.91% -> only comprises 5363 events, i.e. 0.91%
l330 Each of the groups -> Each group
l346 are depicted as blue line in Fig. 6 -> are depicted as blue lines in Fig. 6
l351 later (l385), unstable conditions are characterized by (Nature(ω) < −0.2 Pa/s). Please be consistent in your definition of unstable events.
l354 However, for the infrequently occurring events corresponding to unstable atmospheric conditions, δD observations become at least as important as T observations. -> However, for the infrequently occurring events corresponding to unstable atmospheric conditions, δD observations become at least as important as T observations for all variables except for T itself.
l364 seems -> seem
l392 This real world data are only available -> This real world data is only available
l400 rare -> rarely
l401 are -> is
l417 we need atmospheric isotopologue enabled models that capture as much as possible details of a convective atmosphere. -> we need atmospheric isotopologue enabled models that capture as many details of a convective atmosphere as possible.
l421 cloud-free free troposphere -> cloud-free troposphere
l425 highly resolving models -> high resolution models
l437 observationsare -> observations are
l442 does does strongly further improve -> further strongly improves
l446 we use atmospheric vertical velocity -> we use the atmospheric vertical velocity
l454 indicates to -> indicates; elsewhere available
ll456 you might considering to replace 'remote impact' by '(long-)distance effect'
l457 & 461 'depleted signals' is misleading -> signals of (isotopic) depletion, or (isotopic) depletion signals
l458 the δD observations -> δD observations
l460 "achieving" sounds strange here. Maybe you should write "... as a promising indication that δD can have the same remote impact in the real that it has in our OSSE study for a simulated world."
l470 important contribution -> "important contributions" or "an important contribution"

Hide

AR by Matthias Schneider on behalf of the Authors (18 Jun 2024) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (06 Jul 2024) by Christof Janssen

AR by Matthias Schneider on behalf of the Authors (08 Jul 2024) Manuscript

Journal article(s) based on this preprint

06 Sep 2024

Assessing the potential of free-tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher J. Diekmann, and Kei Yoshimura

Atmos. Meas. Tech., 17, 5243–5259, https://doi.org/10.5194/amt-17-5243-2024,https://doi.org/10.5194/amt-17-5243-2024, 2024

Short summary

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher Johannes Diekmann, and Kei Yoshimura

Data sets

MUSICA IASI water isotopologue OSSE assimilation experiments (used in AMT study) Matthias Schneider, Kinya Toride, and Kei Yoshimura https://radar.kit.edu/radar/en/dataset/DcBNGzfWSFxvkCks?token=HNcZIPnVWFewVDyqQerQ

MUSICA IASI water isotopologue pair product (a posteriori processing version 2) Christopher J. Diekmann, Matthias Schneider, and Benjamin Ertl https://doi.org/10.35097/415

Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher Johannes Diekmann, and Kei Yoshimura

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Despite its importance for extreme weather and climate feedbacks, atmospheric diabatic heating rates are not well constrained. This study assesses the potential of novel tropospheric water vapour isotopologue satellite observations for improving the analyses of latent heating rates. We find, that the impact of the isotopologues is small for events with weak latent heating rates, but significant for strong latent heating events, which have the strongest societal impacts (storms, flooding).


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