the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Jan 2026
Exploring the applicability of Censored Shifted Gamma Distribution (CSGD) error model to radar based rainfall nowcasts: A UK case study
Abstract. Radar based rainfall nowcasting plays a critical role in hydrological operations such as stormwater management and flood early warning. Compared with Numerical Weather Prediction (NWP), it offers higher short-term accuracy and lower computational costs. However, operational uptake remains constrained by two key challenges: (i) uncertainties in nowcasting algorithms and (ii) discrepancies between radar rainfall estimates and ground based measurements. Focusing on the latter, this study explores the potential of the Censored Shifted Gamma Distribution (CSGD) error model to adjust high-resolution radar nowcasts using gauge observations, thus improving their hydrological applicability. The proposed framework involves calibrating both climatological and conditional CSGD models at gauge locations and interpolating parameters across the study area. Deterministic and ensemble nowcasts generated by the Short-Term Ensemble Prediction System (STEPS) are subsequently adjusted using linear and non-linear CSGD models. In this process, predicted rainfall intensities are transformed into cumulative distribution functions (CDFs), enabling probabilistic nowcasting. The median of the CSGD-derived distributions is then applied as the adjusted rainfall intensity, improving alignment with ground observations. Results suggest that combining STEPS ensemble nowcasting with the non-linear CSGD model generally yields the best performance, with error reductions approaching 6 % at the 6 h lead time (hourly scale) and at the 3 h lead time (5 min scale) and uncertainty reductions approaching 20 % across selected events. These findings demonstrate the potential of extending the CSGD method – originally developed for daily satellite precipitation estimation – to hourly and sub-hourly timescales. This advancement enhances the reliability of radar based predictions and their value for hydrological decision-making.
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RC1: 'Comment on egusphere-2025-4590', Anonymous Referee #1, 25 Jan 2026
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AC2: 'Reply on RC1', Li-Pen Wang, 02 Mar 2026
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We would like to thank the detailed and constructive comments from the Reviewer #1. We have prepared and attached a point-by-point response to all comments.
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AC2: 'Reply on RC1', Li-Pen Wang, 02 Mar 2026
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CEC1: 'Comment on egusphere-2025-4590 - No compliance with the policy of the journal', Juan Antonio Añel, 07 Feb 2026
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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
You have archived your the nowcasting framework pySTEPS in readthedocs.io, and the CSGD error-modelling code in GitHub. Unfortunately, none of these sites are acceptable repositories. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, the current situation with your manuscript is irregular. Please, publish your code 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, as we can not accept manuscripts in Discussions that do not comply with our policy. Also, please include the relevant primary input/output data.Also, the 'Code and Data Availability’ section 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 EditorCitation: https://doi.org/10.5194/egusphere-2025-4590-CEC1 -
AC1: 'Reply on CEC1', Li-Pen Wang, 11 Feb 2026
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Dear Executive Editor,
Thank you very much for your careful assessment of our manuscript and for drawing our attention to the requirements of the Geoscientific Model Development Code and Data Policy. We sincerely apologise for not fully complying with the archival requirements in the initial submission. We agree that long-term repositories with persistent identifiers (e.g. DOIs) should have been used for code and data archiving.
We have now taken the following corrective actions:
- The version of pySTEPS used in this study has been archived on Zenodo and is available at: https://zenodo.org/records/15860833.
- The source code for the CSGD_error_model method that we adopted in our nowcasting application had been archived on Zenodo and is available at: https://zenodo.org/records/15485071
- The full source code and the derived CSGD parameters corresponding to all deterministic and ensemble nowcast outputs have been archived at: https://doi.org/10.5281/zenodo.17984774 (this has been provided in the Code and Data section).
Regarding input data:
- The NIMROD radar data are available via the Centre for Environmental Data Analysis (CEDA) for non-commercial research use upon registration and acceptance of the UK Met Office licence agreement. Due to licence restrictions, redistribution of the raw NIMROD data is not permitted. Users are therefore referred to CEDA to obtain the data directly.
- The rain gauge observations used in this study are from MIDAS Open v202407, available via CEDA at: https://catalogue.ceda.ac.uk/uuid/c50776e4903942cdb329589da70b83fe/, and its DOI: https://doi.org/10.5285/c50776e4903942cdb329589da70b83fe.
We have drafted a revised Code and Data Availability section as below and will add the corresponding references to the bibliography to ensure full compliance with the GMD Code and Data Policy.
Kind regards,
Li-Pen Wang
on behalf of all co-authors
A draft of the revised Code and Data Availability section:The computational framework used in this study consists of (i) the pySTEPS nowcasting library and (ii) the CSGD_error_model adopted in this work. The version of \textbf{pySTEPS} used in this study had been archived on Zenodo and is available at: https://zenodo.org/records/15860833, and the version of the CSGD_error_model had been archived on Zenodo and is available at:
https://zenodo.org/records/15485071. Both repositories provide permanent, versioned archives with persistent DOIs to ensure long-term accessibility and reproducibility.The input radar data used in this study are from the NIMROD radar composite archive, available via the Centre for Environmental Data Analysis (CEDA). The data are accessible for non-commercial research use upon registration with CEDA and acceptance of the UK Met Office licence agreement. Due to licensing restrictions, redistribution of the raw NIMROD data is not permitted. Users wishing to reproduce the results should obtain the data directly from CEDA under the applicable licence terms.
Rain gauge observations were obtained from MIDAS Open v202407, available via CEDA at: https://catalogue.ceda.ac.uk/uuid/c50776e4903942cdb329589da70b83fe/ (DOI: https://doi.org/10.5285/c50776e4903942cdb329589da70b83fe).
The source code of the proposed work and all derived CSGD parameters corresponding to the deterministic and ensemble nowcast outputs have been archived at: https://doi.org/10.5281/zenodo.17984774. Because the complete set of nowcast output fields is extremely large, these are not archived in their entirety. However, the archived CSGD parameters, together with the published source code and the original input data (obtainable via CEDA), allow full regeneration of all deterministic and ensemble output fields.
Citation: https://doi.org/10.5194/egusphere-2025-4590-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 11 Feb 2026
reply
Dear authors,
Many thanks for your reply. We can consider the current version of your manuscript in compliance with the Code and Data Policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-4590-CEC2
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AC1: 'Reply on CEC1', Li-Pen Wang, 11 Feb 2026
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The authors developed a framework to combine a radar-based rainfall error model with rainfall nowcasting. The authors demonstrate that applying the non-linear CSGD model to rainfall ensemble nowcasting generally yields the best performance. This research enhances the reliability of radar-based rainfall nowcasting. I would recommend a major revision given the comments below:
1. CSGD has been applied to sub-daily or hourly time scale rainfall error modeling, such as
Peng, K., D.B. Wright, Y. Derin, S.H. Hartke, Z. Li, J. Tan, A Novel Near-Realtime Quasi-global Satellite-Only Ensemble Precipitation Dataset, Water Resources Research, 2025.
Li, Z., D.B. Wright, S.H. Hartke, D.B. Kirschbaum, S. Khan, V. Maggioni, Pierre-Emmanuel Kirstetter, Toward A Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products based on GPM DPR, IEEE Transactions on Geoscience Remote Sensing, 2023.
I would not recognize the " potential of extending the CSGD method–originally developed for daily satellite precipitation estimation– to hourly and sub-hourly timescales" as a major finding in this research.
2. Scheuerer et al. (2015) developed CSGD to be used in precipitation forecasting. Could the authors clarify the rationale for applying an error model calibrated using historical radar observation time series to rainfall nowcasts, rather than calibrating the CSGD model directly on the nowcasted rainfall fields? The latter approach appears more direct for addressing precipitation errors that originate from both radar measurements and the nowcasting model itself. Under the current framework, the capacity to mitigate errors specifically associated with rainfall nowcasting seems limited.
3. Since CRPS is the objective function of CSGD and is also one of the widely used metrics to evaluate the accuracy of ensemble prediction, I would recommend the authors to report the CRPS value as well for the comparison between linear & non-linear CSGD. It can also be used to evaluate ensemble nowcasting accuracy.
4. Can the authors explain why, in Figs 8 & 9, the RMSE of CSGD is higher at a lead time of 1-3 hours? I would recommend the authors to report the evaluation for CSGD at the initial time step (i.e., lead time=0min, no nowcasting applied), so that we can investigate whether the error was propagated from the initial.
5 I would recommend that the author provide more clarification in the CSGD and the nowcasting model's performance in Tables A1-A6. As for different nowcasting methods, different CSGD models, and different metrics, the performance varies. This may imply some shortcomings in the current model that can be further improved in further study.
6. The authors selected the median as the adjusted rainfall intensity for comparison. The median is likely to smooth out the extreme values. I would recommand use ensemble-based metrics to evaluate the ensemble accuracy. Both CSGD and ensemble nowcasting are good tools for ensemble-based decision making. Only focusing on median accuracy may not be a comprehensive evaluation.
Minor revision:
1. I recommend that the authors revise figure3 into three boxes to be consistent with 3.2-3.4 subtitles, so that the authors would better clarify how the three parts are connected.
2. L442, the notation for RMSEI90 needs to be corrected.