This study combines different soil moisture and atmospheric data products to evaluate land-atmosphere coupling within the classical CTP-HI framework. The scientific approach and methods used are sound, and the results will be of interest to the land-atmosphere interactions community. Although the scientific elements are strong, it was at times difficult to clearly grasp what they authors were trying to achieve and communicate. I believe that the paper could benefit from some minor revisions that would help strengthen the narrative and better highlight its key points. These are summarized by my comments on the abstract but extend to the rest of the paper's discussion as well:

Abstract Revisions

The abstract is rather long and fails to adequately set up the study's main goals. Many elements are introduced rather haphazardly and readers may struggle to connect the dots. Some sentences are also repetitive, leading to greater confusion. For example, the abstract begins by stating that "this research assesses the impact of different soil moisture datasets on the classification and distribution of L-A coupling regimes." Then, the following sentence states that the goal is to "examine the persistence of dry and wet coupling regimes... exploring how soil moisture influences coupling classification." Although these sentences are certainly related, it's not clear then whether the main goal is to assess differences between the SMAP data products or to more broadly evaluate soil moisture coupling. The term "persistence of dry and wet coupling regimes" is also introduced without much context, though it seems like "persistence" is a central concept to the study and how the authors are thinking about coupling. Despite the title and first few sentences of the abstract setting up soil moisture and the different SMAP data products as the main focus of the study, the bulk of the abstract is spent discussing the need for consistent and unbiased observations of the atmospheric state and the merged reanalysis product the authors created. 

After rereading the abstract a few times and digesting the rest of the manuscript, here are my suggestions for restructuring:

1. Start off with: "In recent years, there has been a growing recognition of the significance of Land-Atmosphere (L-A) interactions and feedback mechanisms and their importance for weather and climate prediction." (no change)
2. Lead into a sentence explaining why L-A coupling regimes are useful/important/of interest: e.g., "L-A coupling regimes are a useful framework for understanding..." 
3. Then set up the study's focus and contribution with respect to data products: e.g., "Characterizing and studying L-A coupling regimes requires consistent and unbiased observations of surface conditions and the atmosphere..." 
4. Now state the main goal of the study, succintly in one sentence: e.g., "We compare the classification and distribution of L-A coupling regimes across different soil moisture datasets by computing the lag correlation between the SMAP Level 3 and Level 4 soil moisture products and Convective Triggering Potential (CTP) and Humidity Index (HI) from a merged reanlaysis product we develop."
5. 1-3 sentences for findings: e.g., "We find that the persitence of dry and wet coupling regimes during the time period of the study can be understood through..." 
6. End with sentence on significance: "These findings lay the groundwork for understanding the sensitivity of drought evolution to soil moisture variations by gaining insight into the quantification of coupling strength, thereby providing critical insights for future drought modelling and prediction efforts." (second part of the sentence is repetitive and can be removed)

Other Clarifications

1. Timescales of Coupling (L393-394): My understanding is that the CTP-HI framework is typically used to evaluate land-atmosphere coupling on diurnal timescales. In particular, the original Findell and Eltahir papers focus on how soil conditions influence the evolution of the early morning atmosphere. In this study, the authors evaluate coupling between soil moisture and the CTP-HI metrics by computing the lagged correlation over a 10-day average. In lines 393-394 the authors state this is because "reliable weather predictability is generally limited to 10 days." While this is true for numerical weather prediction, this argument seems less relevant for a LA study. I think this is an interesting aspect of the paper that could be expanded on. Please provide more context and justifiation for the timescale of coupling and maybe highlight some other works that have evaluated coupling over this timescale. I believe some of the cited works are relevant here; a review paper some of the authors were involved in (Santanello et al., 2018) also has an excellent discussion of this. 
2. Time Period of Study (L290-292): The abstract states that the study "examine the persistence of dry and wet coupling regimes over two decades (2003–2022), exploring how soil moisture influences coupling classification." This seems a little misleading given that SMAP is only available for the last eight years of that time period. I was a little confused by what "Since soil moisture measurements are only needed for the classification period, the time series of coupling classiciation can still cover the entire time series of CTP-HI from 2003 to 2022." in Lines 290-292 meant. Should this instead say "Since soil moisture measurements are only needed for the lagged correlations?" Please explain and also be more careful about stating the time period of the results in the rest of the study.
3. Time of CTP-HI "Measurements" vs. AIRS (375-381): I think that some of this discussion about the time taken from the reanalysis datasets for CTP and HI metrics should go earlier in Methods (Section 3.1). Given that readers are told the AIRS instrument overpass is for 1:30 AM local time, this naturally raises questions about when CTP and HI are being evaluated since it wouldn't make sense to analyze CTP and HI at 1:30 AM. It would be better to not have to wait so long to have those questions answered and mention the sunrise time earlier.
4. Defining Persistency (L125-126): The quantity "persistency" is central to the study but, since its only described conceptually, it can be hard to understand and remember what exactly it represents and how it is computed. Given that the quantity is not defined with an equation, it maybe be helpful to readers to refer to it as a probability (if I'm understanding it correctly) in some of the figures and when it is first discussed in the results. Perhaps representing it as a probability (%) in Figure 6 could make that more clear?

Minor Edits

1. Figure 3a: Not sure having all the scatterplots displayed here is useful, given that there are so many and requires careful consideration of the axes. Perhaps this figure would be more readable as a table? 
2. Line 36: Maybe a citation for ECV for those who are unfamiliar?

Overall, this was a well-performed study, and I am looking forward to the publication of this work. Some minor improvements on the readability of the manuscript would go a long way in helping the paper better reach its intended audience.

Review

Journal: HESS

Title: Deducing Land-Atmosphere Coupling Regimes from SMAP Soil Moisture

Authors: Makhasana, Santanello, Lawston-Parker, and Roundy

The paper examines the land-atmosphere coupling strength obtained from combining SMAP L3 or, separately, L4 soil moisture data with estimates of the convective triggering potential (CTP) and humidity index (HI) for the lower troposphere.  The CTP and HI estimates are from a merged dataset created by the authors using Triple Collocation from three major atmospheric reanalysis datasets.  The authors find that the CTP and HI estimates from the merged reanalysis dataset outperform CTP and HI estimates obtained from the individual reanalysis datasets (when compared to reference CTP and HI derived from radiosonde observations and, separately, AIRS satellite retrievals).  The authors further find that SMAPL4 demonstrates stronger persistence of the wet and dry coupling regimes as compared to SMAPL3 and suggest that SMAPL4 may offer a robust approximation when assessing land-atmosphere interactions.

Overall, the manuscript has the potential to be an important contribution, but in its current form it falls short, as outlined in the comments below. 

I recommend that the manuscript be returned to the authors for MAJOR revisions.

Comments:

1) It is unclear how the objective of the study relates to the results.  The objective of the study is described as follows:

Lines 85-87: “The goal of this comparative study is to uncover how soil moisture, as detected through direct satellite observations and assimilated data products, influences L-A coupling strength across the globe.”

Lines 470-471: “The goal of this analysis is to explore the role of soil moisture from SMAP in quantifying the L-A coupling strength across the globe.”

The key finding of the paper, however, appears to be related to the *difference* in the estimated coupling strength between SMAPL3 and SMAPL4 (see comment 2).  This result does not quite match the formulation of the objective.  The objective suggests that we will learn “how soil moisture [] influences L-A coupling strength across the globe.”  But the results only compare the different coupling strength estimates obtained for SMAPL3 and SMAPL4.  The results do not examine the role of soil moisture as such in determining coupling strength, nor do they validate the coupling strength estimates.  If the difference between SMAPL3 and SMAPL4 soil moisture could be interpreted as the error in the soil moisture estimates, then the results would examine the impact of the *error* in soil moisture on the estimates of coupling strength (rather than the impact of *soil* *moisture* on coupling strength as claimed).  However, for the obvious reason of SMAPL3 and SMAPL4 being derived from the same sensor, the difference between SMAPL3 and SMAPL4 is not a good estimate of the error in the soil moisture data.

2) One of the key findings appears to be that “SMAPL4 demonstrates stronger persistence of the wet and dry coupling regimes as compared to SMAPL3. […] This suggests that SMAPL4's approach may offer a robust approximation when assessing land-atmosphere interactions, ..” (Lines 21-24; see also Lines 489-497).  The implication here is that SMAPL4 is somehow better than SMAPL3 for the purpose, but the rationale for this remains unclear.  Just because SMAPL4 results in stronger coupling estimates does not make these stronger estimates more correct.  There is some discussion about this in Lines 498-499: “..the representation of strong coupling might not always accurately mirror the complexities of real-world environmental conditions..”.  However, the above key finding in the Abstract and elsewhere read as if there is no such caveat.  And even if, hypothetically, the caveat could be ignored, the authors do not explain *why* they think that stronger coupling is likely to be a better estimate.

3) Related to comment 2): The authors suspect that the larger number of samples available from SMAPL4 explains the stronger coupling estimates (Lines 491-494).  Since the strength of the coupling is measured by the *persistence* of the coupling regime, I am not surprised that SMAPL4 results in stronger coupling estimates, simply because temporal auto-correlation of SMAPL4 soil moisture estimates is much higher than that of SMAPL3 estimates, owing to the fact that SMAPL4 soil moisture is partly derived from a land surface model and therefore less subject to random noise.  This explanation is perhaps hidden in the authors’ language about “the unique characteristics of the SMAPL4’s [sic] assimilation process.”  (Lines 22-23)  However, the link is not obvious and should be examined and discussed more explicitly.

4) Related to comment 3):  Figure 7 and Line 430: “The SMAPL4 dataset, with its higher number of wet regime classifications, demonstrates a greater likelihood of days being categorized as wet.”  The logic here seems backward to me.  On average, SMAPL4 is wetter than SMAPL3, which is a consequence of the different approaches to soil moisture estimation in L4 and L3.  (It is unfortunate that the two estimates, despite coming from the same project, differ in their climatology, but such is the state of our knowledge of soil moisture.)  This climatological difference is clearly shown in Figure 7c.  But has this climatological difference been accounted for in the choice of CTP-HI classification parameters?  The manuscript is not clear about this.  The results (Fig 7a) suggest that the climatological difference between SMAPL4 and SMAPL3 is not considered in the CTP-HI classification.  If so, then it is not surprising that SMAPL4 leads to more “wet regime” classifications.  This needs to be examined further and clarified in the manuscript.

5) In Line 12 and elsewhere, the authors state that they “examine the persistence of dry and wet coupling regimes over two decades (2003-2022).”  But SMAP data are available from April 2015 only.  Are the coupling strength results for the period starting April 2015?  This is very unclear in the manuscript. 

6) The explanation of the methodology CTP-HI-SM classification approach should be improved.  For example, Fig 1 talks about the “CTP-HI-SM space”, but it remains unclear how SM enters the graphic on the right. In this graphic, CTP is on the abscissa and HI on the ordinate.  But is SM shown in the shading?  This is left to the reader’s imagination.  Is the CTP-HI-SM space in the right-hand graphic assembled by aggregating over space and/or time?  Related to this, the text in Lines 111-115 is a bit too brief to be understood without referring to Findell and Eltahir (2003) and/or Roundy et al 2013. 

7) The selected references are often inappropriate. 

- The Triple Collocation references (Lines 76-77) consist of three recent applications, at least two of which are highly specific regional studies.  Gruber et al (2017), which is cited elsewhere, would be more appropriate, or perhaps better still would be the review paper by Gruber et al (2020) doi:10.1016/j.rse.2020.111806 and/or the seminal paper by Stoffelen et al (1998) doi:10.1029/97JC03180.

- Line 223: Ochege et al (2017) is not appropriate as the introductory reference for MERRA-2.  The relevant reference is Gelaro et al (2017), which appears in the following line.

- Line 229: Centella-Artola et al (2020) is not appropriate as the introductory reference for CFSR.  The relevant reference is Saha et al (2010, which appears a few lines later.

8) The use of land surface observations (soil moisture, snow, precipitation) is quite different across the three reanalysis datasets used here.  This information should be included in the brief introductions of the reanalysis datasets (sections 3.1.1, 3.1.2, and 3.1.3).  In addition to the screen-level obs, ERA5 also assimilates soil moisture retrievals from spaceborne scatterometers, which is not mentioned in section 3.1.3.  CFSR and MERRA2, on the other hand, use observation-based precipitation to force the land model within the reanalysis system.

9) Section 3.2 is missing information on how the SMAPL3 quality flags are used, if at all.  Are SMAPL3 screened when they are flagged for less-than-optimal quality?

10) Figures 4, 5, and 9: Since the individual regions cover very different total areas, it is difficult to derive the skill or coupling metrics for the entire globe from visual inspection.  In these bar charts, I suggest adding a 7-th group of bars with the metrics for the entire globe.

11) There are many typos and grammatical errors throughout the manuscript.  See “Editorial comments” below for a sampling.  While the impact of these errors on the readability of the paper is relatively small, they reflect poorly on the quality of the study.  Have the senior coauthors (who are all native speakers) proofread the paper?

Minor comments:

1. a) Line 17: “Despite significant correlated errors within the individual reanalysis datasets, ..” Do you mean “Despite significant error correlations across the individual reanalysis datasets, ..”?  Or are you referring to “temporally (auto-)correlated errors within the individual reanalysis datasets”?  Please clarify the exact error correlation implied here.
2. b) “HI measures low atmospheric moisture levels” (Line 52) This is not about “low [as opposed to high] moisture levels” (that is, dry vs. wet air), right?  Do you mean “HI measures moisture levels in the lower troposphere”?  Or, perhaps better: “HI measures moisture *content* in the lower troposphere”.   (“Levels” is easily confused with the “model levels” used in atmospheric models.)
3. c) Line 134 says that “the atmospheric controlled and transitional regimes are combined into one regime”, but Fig 7 then distinguishes between the transitional and “atmospheric controlled” regimes. This is contradictory.
4. d) Line 115, equation (1): Missing plus sign after “a_i”?
5. e) Lines 168-169, equations (2) and (3): Missing definition of symbols \mu and \sigma
6. f) Line 176: “..differences between two variables..” Do you mean “..differences between two datasets..”? (That is, differences in the estimate of, say, CTP from different reanalysis datasets.)
7. g) Lines 189-190: “Additionally, the study comprises a 30-day centered window (15 days on either side of the compound event) that removes the effect of seasonality.” This information comes too late and should be moved up. It is fundamental to the success of Triple Collocation that the seasonal cycle is removed from the data first.
8. h) Line 221: MERRA-2 is an atmospheric reanalysis, and the variables of interest for this paper are estimates of atmospheric conditions (CTP and HI). Therefore, description of MERRA-2 should mention the GEOS AGCM, not just the Catchment land model. In the context of the present study, the Catchment model is much more relevant as the land surface model underpinning the SMAPL4 land data assimilation system.
9. i) Line 276: The “resolution” of the (enhanced) SMAPL3 data is not 9 km. Unfortunately, there is some misleading information on the NSIDC web documentation. The true  resolution of the “enhanced” L3 retrievals is closer to ~30 km. 
10. j) Figures 3, 4, 5, 7, and 8 are missing units for CTP and HI.
11. k) Figure 3a is a scatterplot of *differences* (or errors vs obs). Accordingly, x- and y-labels should read “CFSR minus IGRA”, “MERRA-2 minus IGRA”, and “ERA5 minus IGRA”.
12. l) The numbers in Table 2 should all have the same number of decimals. I think two decimals (or integer values in percentage terms) would be sufficient and much easier to read. (A disclaimer could be added that the percentage values may not add to 100 because of roundoff error.) 
13. m) Line 339: “The data is [sic] merged following equation (10).” This equation provides the objective function for determining the optimal weights. It is not the equation used to merge the datasets, which is presumably:   CTP_merged = w_M2 * CTP_M2 + w_ERA5 * CTP_ERA5 + w_CFSR * CTP_CFSR.
14. n) Line 350: “discrepancies” with respect to what?
15. o) Line 375: Clarify if ~1:30AM is the *local* overpass time.
16. p) Line 378: “Fig 4” seems to be the wrong reference here
17. q) Line 402: “a more reliable predictor” - more reliable than what?
18. r) Figure 7c,d: What do you mean by “Saturate Soil Moisture”? Do you mean “soil moisture in units of relative saturation” (or “wetness” units)?
19. s) Figure 7b: What exactly do you mean by “Joint Probability of CTP-HI-SM space”? How is the graphic showing a “probability”?
20. t) Lines 443-446: It would be helpful to insert “Fig 8a” and “Fig 8b” here to help the reader identify the specific part of the graphic that illustrates the statements made here.
21. u) Figure 8: It would be helpful to add “CTP” in the top row and “HI” in the bottom row of the graphic.
22. v) Line 474: Should “Figs. 7 and 8” read “Figs. 6 and 8”??
23. w) Lines 519-520: “For instance, Xu (2020) has shown that SMAPL4's bias is significantly reduced,..” Reduced with respect to what?
24. x) Lines 521-522: “.. which showed that SMAPL4 captures spatial and temporal soil moisture variations more reliably across the United States.” More reliably than what?
25. y) Lines 527-528: “The SMAP provides enhanced depiction of L-A coupling through dynamic soil moisture data, offering improved drought monitoring and weather prediction.” This statement is not supported by the results or a reference.
26. z) Lines 543-544: “Despite this, the merged dataset still demonstrates a more accurate reflection of in-situ and satellite observations of CTP and HI,..” More accurate than what??

Editorial comments:

Lines 47-49: Delete “to illuminate the L-A coupling”

Line 127: Capitalize “Hi” - - > “HI”

Line 176: Equations (4)-(6) use (curly) “braces” not “brackets”.

The following sentences are a sampling of the grammatical errors or otherwise difficult-to-read sentences mentioned above:

Line 20: “a higher lag-correlation between soil moisture and the CTP-HI metrics contribute to the persist coupling behaviour”

Lines 78-79: “Therefore, using the TC method to merge reanalysis data sets of CTP and HI based has the potential to provide..”

Lines 108-111: “In the revised CTP-HI framework [..], the interplay between soil moisture and atmospheric conditions is distinguished into four specific coupling regimes: wet coupling, dry coupling, transitional, and atmospherically controlled; and summarize the complex relationship between soil moisture content and the feedback from the land to the atmosphere in a generalized context.”

Lines 200-202: “To assess the performance of merged CTP-HI the analysis also includes Atmospheric Infrared Sounder Version 7(AIRSv7) satellite remote sensing and radiosonde observations from Integrated Global Radiosonde Archive Version 2 (IGRA2).”   

Lines 459-460: “However, when considering the impact of sample size, the difference in coupling strength is dimensioned.”   [What does this mean??]

Lines 506-507: “In synthesizing the comparison between SMAPL3 and SMAPL4, as depicted in Fig. 7c, highlights the differences in soil moisture representation arise mainly from their distinct constraints and processing methodologies.”

Line 555: “Such stronger persistence of wet and dry coupling regimes, as observed in SMAPL4 is not only a result of a greater number of observations, but it possibility due to the distinctive assimilation techniques employed in the SMAPL4 dataset.”