the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jul 2026
Development of a dual-polarization Zdr radar operator with ice-phase hydrometeor classification for variational assimilation systems (RadDualIceVar v1.0)
Abstract. A differential reflectivity (Zdr) observation operator incorporating ice-phase hydrometeor classification was developed to explicitly account for ice-phase hydrometeor contributions in the data assimilation. This proposed observation operator is then implemented within a three-dimensional variational (3DVAR) data assimilation framework for direct assimilating radar Zdr observations. Its performance was evaluated using both single-observation and cycling assimilation experiments for a typhoon case. Results from the single-observation test indicate that assimilating Zdr introduces additional microphysical constraints, leading to more effective adjustments of hydrometeor compared with reflectivity-only assimilation. In the cycling assimilation experiments, the combined reflectivity and Zdr (RFZDR) configuration produces a more physically consistent representation of reflectivity and Zdr, along with a more realistic vertical and horizontal distribution of hydrometeors. These improvements propagate into short-term precipitation forecasts, with RFZDR exhibiting lower root-mean-square errors, enhanced spatial consistency, and gains in heavy precipitation metrics.
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CEC1: 'Comment on egusphere-2026-2230 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Aug 2026
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AC1: 'Reply on CEC1', Liu Yi, 12 Aug 2026
Dear Juan A. Añel,
Thank you for pointing out the issues regarding the GMD Code and Data Policy.
We have now archived the WRFDA code, FNL data, and radar data required for this study in Zenodo. The complete archive is available at: https://doi.org/10.5281/zenodo.21896503
We will also update the “Code and Data Availability” section and the corresponding references in the manuscript.
Please let us know if any further adjustments are needed.
Best regards,
Liu Yi
Citation: https://doi.org/10.5194/egusphere-2026-2230-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 12 Aug 2026
Dear authors,
Thanks for addressing this issue so quickly. I have checked the repositories and we can consider now the current version of your manuscript in compliance with the code policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2026-2230-CEC2
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CEC2: 'Reply on AC1', Juan Antonio Añel, 12 Aug 2026
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AC1: 'Reply on CEC1', Liu Yi, 12 Aug 2026
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RC1: 'Comments on egusphere-2026-2230', Anonymous Referee #1, 31 Aug 2026
Overall/General Comments:
The authors present advancements to the ice-phase hydrometeor treatments of dual-polarization radar observations in forward operators in a three-dimensional (3DVar) approach. These changes are incorporated into the tangent-linear (TL) and adjoint (AD) operators and they carry out
several experiments that assimilate reflectivity at the horizontal polarization, radial velocity, and differential reflectivity (Z DR ) for Typhoon Doksuri (2023) in the Eastern Pacific Ocean. While I think there is potential value in this topic, some significant comments on the model and
experiment configuration, observations, and analysis should be addressed, which are detailed below. I also think more careful consideration should be given to formatting, syntax, and citations. Since I believe these revisions would take more than 2 months, I recommend rejection for this manuscript at this time.Specific/Major Comments:
1) From a big picture standpoint, it seems like the main goal of this work is to test/understand if
an operator with special treatment to ice-phase hydrometeors will improve the model, given that
most polarimetric operators do not explicitly treat ice-phase hydrometeors in the same manner? I
think the authors should run experiments with and without the explicit ice-phase hydrometeor
considerations presented in Section 2, considering that the framework should already be present
with the Jung et al. (2008) operators.
2) I have questions about the model setup. A 5-km horizontal grid spacing is used, which is not
typically small enough to resolve polarimetric features such as the K DP , Z DR column, or Z DR
arc/ring. Many other studies that assimilate polarimetric radar data (e.g. Putnam et al. 2019,
Putnam et al. 2021, Chen et al. 2024; Eure et al. 2025) use 3-km or smaller grid spacing. To this
point, the use of a single-moment microphysics scheme in this work (Thompson et al. 2004;
besides cloud ice) also has problems. Due to size sorting and other issues (e.g. Johnson et al.
2016), single-moment microphysics may not be able to replicate any of these polarimetric
signatures and could restrain maximum benefits. Therefore, I ask that the authors reconsider and
rerun these experiments with smaller horizontal grid spacing and a double-moment microphysics
scheme.
3) I am concerned about the lack of assimilation of any other high-resolution observations
beyond the radial velocities, reflectivities, and differential reflectivities included in this study.
From the perspective of operational/real-time forecasts, conducting these experiments without
any baseline conventional observations does not make this work as meaningful or applicable to
operational forecasts. While the 6-hour spin-up period makes sense, I suggest including
conventional observations in all experiments presented.
4) More information needs to be provided on the observation processing. Quantitatively, what
went into the quality control measures? How was ground clutter identified, and were other fields
(such as co-polar correlation coefficient) used to determine clutter? What was the spatialresolution of these radar data? How many radial velocity, reflectivity, and differential reflectivity
observations were assimilated? This is all important information that should be included within
Section 3.
5) More information should be provided on the length scale. I am assuming “0.3” is used for the
vertical covariance localization? Was any value used in the horizontal? Please clarify. The same
is true for the observation errors. It is not clear, but it looks like 5 dBZ and 2 m/s is for
reflectivity and radial velocity? Does the value on Line 280 refer to differential reflectivity
(stated as dBZ instead of dB)? What are the justifications for using the covariance localizations
and observation errors? Did you do any sensitivity testing or refer to prior works? Please specify
in the paper.
6) While this issue points to specific figures and text, throughout the whole paper, the authors
need to be more thorough with the figures and tables included. Valid times and basic information
should be specified in figure captions. For example, “the first assimilation time” in a caption
should specifically refer to “1800 UTC 27 July” in Figure 5. In Figure 4, the location of the
vertical cross section needs to be stated in the caption. Additionally, all figures included in the
paper need to be referenced within the text itself – otherwise, they do not serve a purpose in my
opinion. Some of these specific comments are mentioned below, but the list is not exhaustive.
7) Section 4.1: Is it really accurate to call this a single-observation test if two observations are
assimilated in the RFZDR test? A comparison of 1 reflectivity observation and 1 ZDR
observation for the same location/radar with the same fields shown would be more helpful for
illustrating this point.
8) Figure 5: I struggle to find notable differences in the reflectivity structures between RF and
RFZDR, particularly after the initial/first assimilation cycle. However, Between RF and RFZDR
after the final assimilation time, I do see larger reflectivity values on the southern side of the
eyewall and slightly weakened reflectivity on the east-northeastern side of the eyewall
(comparing Fig. 5g and 5h), but then also stronger reflectivity for the cell due southeast of the
eyewall. Further justification is needed to determine from this figure than RFZDR is closer to the
observed reflectivity than RF. Perhaps zooming in further on some of the heavier convection is
valuable, but it is not plainly discernible to me.
9) Figure 10: More information is needed on how RMSIs are calculated here. A basic formula
should be provided. Why are RMSIs at the 30-min data assimilation intervals not included?
Additionally, provided that many microphysics schemes are prone to biases in simulated
reflectivity, I believe the authors should consider a bias-corrected RMSI metric that rather looks
at the differences of individual reflectivity innovations from a domain/time-averaged value rather
than the raw innovations. Therefore, the possibility of a consistent over or underestimation of
reflectivity in H(x) is eliminated. See Equation 3.2 Dowell and Wicker (2009) for the
mathematical implementation.10) Naturally, both 3DVar and EnKF DA have advantages and disadvantages in convective-scale
DA studies like this one. I am suspicious that using the 20-day climatology B in these
experiments could be a cause for the relatively small differences in analyses and forecasts
presented throughout the results. How do these static background errors compare to the
observation errors? I believe the authors should comment on the potential limitations of a
variational method for this case and further their discussion in the conclusions.
11) Figure 14: I am struggling to find differences between RF and RFZDR here and the provided
analysis appears mismatched from the figures. It is possible that changing the precipitation
colorbar (100-150 for purple) or zooming in on the heavy-precipitation region would help. Based
on the heavy precipitation (>100 mm) in CRTL, doesn’t this compare better to the observations
than RF and RFZDR?Technical/Minor Comments:
Line 17: “assimilating radar” -> “assimilation of”
Line 19: “hydrometeor” -> “hydrometeors”
Line 20-21: “physically consistent” -> “physically-consistent”
Line 23: “heavy precipitation” -> “heavy-precipitation”
Line 26: I would prefer “e.g.” before “Bauer” as numerous papers in the literature discuss this topic.
Line 27: WRF requires a citation.
Line 36: Add “the” before “horizontal”
Line 36: “horizontally” -> “horizontally-“
Line 37: “vertically” -> “vertically-polarized”
Line 39: “dynamically consistent” -> “dynamically-consistent”
Line 48: Replacing “explicitly” with “directly” feels more accurate with the point the authors are
conveying.
Line 49-50: “Because cross-covariances between the observed polarimetric quantities and all
relevant model state variables are often weak” While insufficient representation can certainly be
a problem between polarimetric observations and model state variables, I am not sure it is true to
say that they are weak. For example, I would expect observed ZDR to have a decently positive
correlation with model state mixing ratios of rainwater.
Line 53: The EnKF needs a citation.Line 55-57: I believe a mention and/or brief discussion of the newer polarimetric operators in
Zhang et al. (2021) are needed here. Following later information in the introduction, these
operators do account for melting. Additionally, content in the introduction should reflect this.
Line 57: Add “the” before “EnKF”
Line 58: “convective scale” -> “convective-scale”
Line 67-68: “Most existing studies on dual-polarization radar data assimilation have primarily
focused on warm-rain processes” Is this statement true? Can you provide or discuss examples of
recent/relevant works that explicitly focus on warm-rain processes? Utilization of dual-
polarization radar DA on convection, to me, is not necessarily a warm-rain process only focus,
especially since dual-polarization data by design provide information on hydrometeor phase.
Line 108: Add “the” before “horizontal”
Line 109: Same comment as above.
Line 111: Add “polarized” after “horizontally and vertically”
Table 3: Please provide a description of the table used. Additionally, there is no explicit mention
of Table 3 anywhere in the text.
Table 4: The same comment as Table 3.
Line 245: By what time on 24 July? Please indicate in the text.
Line 246: Around when on 28 July was landfall made? Please indicate clearly in the text.
Line 252: Specifically, what do you mean by “dynamically adjust?”
Figure 1: This figure is not referenced anywhere in the text. Adding “DA” after “radar” would be
helpful. A description of the blue lines should also be included.
Line 259: A citation for this dataset is needed.
Line 277: Was any thinning applied to the radar data? Please include the range gate spacing as
well.
Figure 2: A unit label should be provided or referenced.
Line 282: Figure 2 is not referenced anywhere in the text.
Line 283: Remove “city”
Line 283: Period at the end of the sentence.
Line 286-288: Please connect these two sentences, as the first is not a complete sentence.Line 303-304: “the first column” and “the second column” should be replaced with the specific
subpanel labels (e.g. a,b,c).
Figure 3: Using the same colorbar for all panels despite different values for each column is
misleading, as it makes the increments in a) and d) appear the smallest when they are actually the
largest.
Figure 5: The colorbar should indicate that white values are where reflectivity < 10 dBZ. Please
state valid times associated with the first and last assimilation times.
Line 359-361: Don’t both experiments show a 40-dBZ core here? I do not notice this positional
offset that the authors state. This needs more of a description.
Line 363-365: Units also need to be stated here.
Line 369: “Region A” needs to be clearly defined in the figure caption for Figure 5. Additionally,
state the valid time of the final assimilation time.
Line 369-370: Is this horizontal black line in Figure 5 or Figure 6?
Line 376: Why do you think is the origin or cause for overestimation of ice-phase scattering
contributions? I am curious.
Line 411: Region B also needs to be identified.
Line 427: The first assimilation time should be stated here.
Line 441: When introducing the figure, please state that this refers to Region A (unless that is
untrue).
Line 442: “the vertical cross section” -> “vertical cross sections”
Line 449: “region” -> “Region”
Line 476: What source of observations are used for this analysis? Further information and a
requisite citation would be welcome.
Line 477: “heavy precipitation” -> “heavy-precipitation”
Line 481-482: “RFZDR experiment achieves a lower root-mean-square error” I do not think this
is accurate since RMSIs for accumulated precipitation are not shown anywhere in the manuscript.
Figure 14: Specify which panels are observations and which panels are from the experiments. The valid times need to be included as well.
Line 494: FSS and ETS need citations.
Line 494: Keep figure referencing consistent with journal expectations in the paper, e.g. Fig. 16b vs Fig. 16(b).Line 499-500: Specifically, what do you think led to improved ETSs and FSSs in RFZDR here?
Improved microphysics representation? Some expansion of the analysis in the last sentence here would be welcome.
Line 502-503: Is the full domain used for FSS and ETS computations? If not, this does not need to be specified. However, the time periods need to be explicitly stated in the caption.
Line 508: If you define acronyms for tangent linear and adjoint, they should be used here.
Line 519: “physical consistent” -> “physically-consistent”
Line 650: This line looks blank.References:
Chen, H., and Coauthors, 2024: Assimilation of Water Vapor Retrievals from ZDR columns Using the 3DVar Method for Improving the Short Term Prediction of Convective Storms, Mon. Wea. Rev., 152, 1077-1095, https://doi.org/10.1175/MWR-D-23-0196.1.Dowell, D., and L. Wicker, 2009: Additive noise for storm-scale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911-927, https://doi.org/10.1175/2008JTECHA1156.1.
Eure, K., and Coauthors, 2025: Simultaneous Assimilation of Dual-Polarization Radar and All-Sky Satellite Observations to Improve Convection Forecasts, Mon. Wea. Rev.,153, 2397-2414, https://doi.org/10.1175/MWR-D-24-0157.1.
Johnson, M., Y. Jung, D. Dawson II, and M. Xue, 2016: Comparison of simulated polarimetric signatures in idealized supercell storms using two-moment bulk microphysics schemes in WRF. Mon. Wea. Rev., 144, 971-996, https://doi.org/10.1175/MWR-D-15-0233.1.
Putnam, B., and Coauthors, 2019: Ensemble Kalman filter assimilation of polarimetric radar observations for the 20 May 2013 Oklahoma tornadic supercell case. Mon. Wea. Rev., 147, 2511-2533, https://doi.org/10.1175/MWR-D-18-0251.1.
Putnam, B., and Coauthors, 2021: The impact of assimilating Z DR observations on storm-scale ensemble forecasts of the 31 May 2013 Oklahoma storm event. Mon. Wea. Rev., 149, 1919-1942, https://doi.org/10.1175/MWR-D-20-0261.1.
Thompson, G., R. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519-542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2.
Zhang, G., J. Gao, and M. Du, 2021: Parameterized forward operators for simulation and assimilation of polarimetric radar data with numerical weather predictions. Adv. Atmos. Sci., 38, 737-754, https://doi.org/10.1007/s00376-021-0289-6.
Citation: https://doi.org/10.5194/egusphere-2026-2230-RC1 -
CC1: 'Comment on egusphere-2026-2230', Yi Wang, 03 Sep 2026
This manuscript presents a useful extension of a variational dual-polarization radar observation operator by explicitly incorporating ice-phase hydrometeors and melting-layer effects into the Zdr observation operator. The development of the tangent-linear and adjoint operators, together with the single-observation and real-case assimilation experiments, provides a relatively complete evaluation of the proposed framework. The results suggest that assimilating Zdr can provide additional constraints on hydrometeor distributions and improve short-term precipitation forecasts. The following comments may help improve the clarity and robustness of the manuscript.
General comment
1. The treatment of observation errors for Zdr should be clarified. The manuscript specifies observation errors of 5 dBZ for reflectivity and 2 m s⁻¹ for radial velocity, while a value of 5 dBZ is also used for vertical reflectivity when Zdr is assimilated. Please clarify the effective observation error used for Zdr and explain the rationale for this choice.
2. Please clarify whether the RF and RFZDR experiments use exactly the same variational settings, including the number of iterations, convergence criteria, and other optimization parameters.
3. The terms “more physically consistent” and “more realistic” are used frequently in the Results and Conclusions. Please clarify the specific criteria used to define these improvements and, where possible, distinguish between improved agreement with observations and improved physical realism.Specific Comments
1. In the Abstract,”for direct assimilating radar Zdr observations”is grammatically awkward. I suggest:”for the direct assimilation of radar Zdr observations”. “adjustments of hydrometeor” -->”adjustments of hydrometeors”
2. Please define 3DVAR at its first occurrence as “three-dimensional variational (3DVAR)”.
3. Line 100 ”where, Fmax denotes...” A comma is usually not needed after “where”. It is recommended to uniformly remove commas in places where the text resembles “where, ...”.
4. Line 110 “The horizontally and vertically reflectivities ...” --> “The horizontal and vertical reflectivities”
5. Line 140 “By integrating the DSD in Eqs. (8) and (9), a simplified formula obtained for Z:” -->”By integrating the DSD in Eqs. (8) and (9), a simplified formula for Z is obtained:”
6. Line 186 “For the TL and AD operator,”-->“For the TL and AD operators,”
7. Figure 12: If the “observations” shown in this figure are actually hydrometeor classifications derived from radar observations, please consider replacing “observations” with “radar-derived hydrometeor classification”.
8. Figure captions: The figure captions should be carefully revised for grammatical completeness and clarity. In particular, each caption should clearly identify the variable, experiment, units, observation/model distinction, and relevant time or height information. Figure 6 is one example where the caption could be made more self-contained.Citation: https://doi.org/10.5194/egusphere-2026-2230-CC1 -
RC2: 'Comment on egusphere-2026-2230', Anonymous Referee #2, 14 Sep 2026
This study proposes a framework for assimilating radar horizontal reflectivity and differential reflectivity within the WRF variational data assimilation system. This work extends the assimilation approach of Jung et al. (2008) from an ensemble assimilation system to a variational assimilation system and extends the warm-rain-only treatment in Kawabata et al. (2018) and Chen et al. (2025) to account for ice-phase processes.
However, I have concerns about the experimental design. In its current form, the experiments do not provide sufficiently convincing evidence to support authors’ main arguments and conclusions. Addressing these issues require substantial revisions to the experimental design and additional experiments. The overall writing also needs considerable improvement. Therefore, I recommend rejection of the manuscript in its current form.
Major comments:
1. Experiment design
- The control experiment (CTRL) should assimilate radial velocity.
The radial velocity assimilation is already available in WRF, whereas the primary contribution of this study is the development of new forward operators for assimilating horizontal reflectivity and differential reflectivity. Therefore, to isolate and evaluate the impact of horizontal and differential reflectivity assimilation, the appropriate comparison should be between an experiment assimilating radial velocity only and experiments assimilating radial velocity together with horizontal reflectivity and/or differential reflectivity.
- Need sensitivity experiments to demonstrate the importance of ice-phase processes.
The Introduction identifies the lack of ice-phase processes in previous variational reflectivity forward operators (e.g., Kawabata et al., 2018; Chen et al., 2025) as one motivation for this study. Since the present study explicitly incorporates the ice-phase processes, the experiment design should include a corresponding sensitivity experiment that excludes the ice-phase contribution. Such an experiments would allow the authors to demonstrate whether and to what extend the inclusion of ice-phase processes improves the assimilation and forecast results.
- Static background error covariance
The static background error covariance (BEC) is generated following the National Meteorological Center (NMC) method. One reason that many modern radar assimilation studies favor hybrid or ensemble approaches is that a static BEC may not adequately represent the cross-variable correlations between thermodynamical state variables (e.g., temperature and water vapor) and hydrometeor variables (i.e., rain, snow, and graupel).
Therefore, it is important for the authors to demonstrate whether the generated static BEC contains physically reasonable correlations between the thermodynamical and hydrometeor variables. I recommend including a panel figure showing representative cross-variable correlation structures from the generated BEC, particularly between temperature/water vapor and rain, snow, and graupel.
- RFZDR experiment
It is not clear from the manuscript how the RFZDR experiment is conducted. Are the horizontal reflectivity and differential reflectivity assimilated simultaneously or sequentially? Is the differential reflectivity assimilated as differential reflectivity itself or as vertical reflectivity?
2. Radar data processing
- How is the ground clutter identified and removed? Please provide sufficient details.
- What coordinate system is used for the radar observations in the assimilation? Are the observations retained in polar coordinates or converted to Cartesian coordinates before assimilation?
3. Single observation test
- Need to specify the horizontal reflectivity innovation, and the differential reflectivity innovation.
- Beside the increments of rain, graupel, and snow, it is more important to show the increments of temperature and water vapor. This is particularly important given the use of a static BEC and the potential limitations of static covariance in representing cross-variable correlations.
4. Cycling experiment
- Lines 335–337 and 343–344 state that RFZDR performs better than RF. However, based on Fig. 5, I could not see an overall improvement of RFZDR (Figs. 5d,h) over RF (Figs. 5c,g). The authors should either provide quantitative evidence supporting this statement or revise the discussion to reflect what is actually shown by the results.
5. Figure 7:
- Please add a vertical profile showing sample size at each level, particularly for the observations. The sample size is important for assessing the robustness of the Contoured Frequency by Altitude Diagrams (CFADs).
- Why are CFADs calculated only over Region A rather than over the entire domain or over the region covered by the typhoon? The regional selection may give the impression of cherry-picking a region where the results are more favorable.
- Lines 387–390 state that RFZDR performs better than RF near the melting layer (4–6 km). However, I could not identify such an improvement in Fig. 7d relative to Fig. 7c.
- If the objective is to demonstrate the impact of assimilation using CFADs, I recommend also plotting the CFADs of reflectivity difference, in addition to the CFADs of reflectivity itself. Such a comparison would more directly demonstrate whether the assimilation improves the agreement between the model and the observations.
6. Precipitation verification
- Please clarify the accumulation periods used for the 3-h and 6-h accumulated precipitation?
- What is the precipitation verification dataset?
- The quantitative precipitation verification should be performed over the entire domain or over a region encompassing the typhoon and its precipitation system. Evaluation over only a selected, limited area (Region A or B) is not sufficient to establish the overall forecast impact.
- Additional quantitative evaluation against observed horizontal reflectivity and differential reflectivity would provide more evidence about forecast performance.
- Figure 15a evaluates the 3-h accumulated precipitation forecasts using thresholds of 10, 50, and 100 mm. However, based on the simulated precipitation shown in Figs. 14c and 14d, simulated precipitation at or above 100 mm is not visually discernable. I suspect that only a very small number of grid points exceeding 100 mm in the RF and RFZDR. If so, the FSS/GSS evaluation at 100 mm may not be statistically meaningful. Therefore, this result should not be used as evidence for improvement of RFZDR over RF.
- For Fig. 15b, the 6-h accumulated precipitation evaluation also deserves further examination. Given the similarity between the RF and RFZDR precipitation forecasts and their apparent differences from CTRL in Figs. 14f–h, it is somewhat surprising that the quantitative evaluation shows RF having nearly the same score as CTRL at 100 mm, while RFZDR differs substantially from both. The authors should double check the calculation.
7. One case study is not sufficient to support the general conclusion of this study for two reasons:
- First, the improvement associated with additional ZDR assimilation is not sufficiently clear from the current case.
- Second, one advantage of variational assimilation over hybrid or ensemble assimilation is computational efficiency. This is also mentioned in the Introduction. Then why limited only to one case?
Minor comments:
- Lines 92: Use “Section” instead of “Sect.”.
- Table 5: The acronyms (RV, RF, ZDR) should be defined in the caption.
- Figures and their captions should be improved
- Figure 1: The caption should be more descriptive.
- Figure 2: Please change “represent radars from the cities of Quanzhou, Xiamen, and Shantou city” to “represent radars from Quanzhou, Xiamen, and Shantou”.
- Figure 3: Please mark the observation location in the figures. Also, specify what are qr, qg, and qs in the caption.
- Figure 5: Please specify the first and final assimilation times in the caption. Also, explain what the lines and boxes represent.
- Figure 7: Please specify what the line represents in the caption. I also recommend adding the freezing level to Fig. 7a to add visualization.
- Lines 266–268: which length scale is adjusted, horizontal or vertical? Also, this statement should be placed after the description of background error covariance.
Citation: https://doi.org/10.5194/egusphere-2026-2230-RC2
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
First, in your manuscript you do not provide a repository for the WRF model used in your work. We can not accept this. The policy of the journal clearly establishes that all the code and data necessary to replicate a submitted work must be published in an open repository before submitting it, and adequately cited in the text, including URL and permanent handler (e.g. DOI).
In addition, you have archived the data used and produced in your work in sites that we can not accept (e.g. ucar.edu and cma.cn servers), as they do not fulfil GMD’s requirements for a persistent data archive because:
- They do not appear to have a published policy for data preservation over many years or decades (some flexibility exists over the precise length of preservation, but the policy must exist).
- They do not appear to have a published mechanism for preventing authors from unilaterally removing material. Archives must have a policy which makes removal of materials only possible in exceptional circumstances and subject to an independent curatorial decision,
- In the case of the cma.cn it does not appear to issue a persistent identifier such as a DOI or Handle for each precise dataset.
If we have missed a published policy which does in fact address this matter satisfactorily, please post a response linking to it. If you have any questions about this issue, please post them in a reply.
The GMD review and publication process depends on reviewers and community commentators being able to access, during the discussion phase, the code and data on which a manuscript depends, and on ensuring the provenance of replicability of the published papers for years after their publication. Please, therefore, publish your code and data in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible. We cannot have manuscripts under discussion that do not comply with our policy.
Later, if the Topical Editor decides to continue with the review or publication process of your manuscript and you are requested to upload a new version of it, then The 'Code and Data Availability’ section of your manuscript must also be modified to cite the new repository locations, and corresponding references added to the bibliography.
I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in GMD.
Juan A. Añel
Geosci. Model Dev. Executive Editor