the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 May 2026
BinMod1D v1.0.10: A Python package for explicitly simulating 1D collisional coalescence/breakup processes with corresponding polarimetric radar signatures
Abstract. This paper details a computationally efficient and versatile Python package (BinMod1D v1.0.10) that explicitly evolves spectral bin distributions and corresponding polarimetric radar variables for rain or snow according to atmospheric collisional coalescence and breakup processes. BinMod1D can be executed as a box model, a 1D steady-state model in height, or a full (time and height) 1D column model utilizing multiple particle categories, each of which can have their own densities, aspect ratios, and fall speeds. Forward simulations of polarimetric radar variables are implemented using standard Rayleigh analytic scattering equations. Two-moment (mass and number) or one-moment (mass only) particle interaction calculations follow a source-based approach but with parallelizable just-in-time (JIT) compilation for high performance. BinMod1D box model solutions are validated using analytic solutions of collision-coalescence using a variety of kernels as well as for breakup and the steady-state balance of coalescence with breakup. BinMod1D capabilities are demonstrated through steady-state simulations of rainfall and snow signatures, as well as vertical profiles of diverse meteorological scenarios. Convergence and timing tests are provided for the meteorological scenario of a cloud to rain transition using a realistic collision kernel and fragment distribution. BinMod1D is intended to enable cloud microphysics and weather radar researchers to efficiently simulate vertical profiles of complex weather events. Such a tool can be used to provide reference solutions for training machine learning models and validating various retrieval methodologies.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(4967 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4967 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2026-2004', Anonymous Referee #1, 06 Jun 2026
- AC1: 'Reply on RC1', Edwin Dunnavan, 08 Jul 2026
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RC2: 'Comment on egusphere-2026-2004', Anonymous Referee #2, 11 Jun 2026
## Summary
This paper presents BinMod1D, a Python package for simulating collisional coalescence and breakup processes in precipitation, with corresponding polarimetric radar signatures. The model can run as a box model, 1D steady-state, or full time/height column model.
Overall Assessment: The paper is well-written and presents valuable tools for the cloud microphysics and radar meteorology community. The work is solid and the implementation is useful. Future work demonstrating specific applications and case studies will help the community better understand where this modeling approach fits within the broader landscape of microphysical modeling tools and how it can generate the most scientific impact.
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## Comments
General Comment on Figures 1-3: The text describing the methodology is quite dense, and while the figure annotations are good, the narrative could be expanded to better guide readers through interpreting these complex diagrams. Consider adding:
- Step-by-step walkthrough of what each panel shows
- More explicit connections between figure elements and the mathematical terms in equations
- A "how to read this figure" text, especially for Figure 1's complex gain/loss regions
- Clearer transitions from figure to text and backThe figures contain important conceptual information about the numerical scheme, but readers may struggle to extract insights without more narrative scaffolding.
### Discussion
The paper presents a solid technical contribution, but would benefit from expanded discussion of:
- Where BinMod1D fits within the existing ecosystem of microphysical modeling tools
- Specific use cases where this approach has advantages over alternatives.
- Potential applications in operational radar retrieval development or research workflows
- Future work demonstrating community impact through real-world case studiesThis would help readers understand not just *what* the tool does, but *when* and *why* they should use it.
---
## Minor Comments
- Line 127: Missing text - should read "model, due to" (I think)
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Citation: https://doi.org/10.5194/egusphere-2026-2004-RC2 - AC2: 'Reply on RC2', Edwin Dunnavan, 08 Jul 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-2004', Anonymous Referee #1, 06 Jun 2026
General Comments
This manuscript presents BinMod1d, a package for atmospheric coagulation and breakup using spectral methods. The paper is clearly written and the figures are intuitive. Overall, this is a useful software contribution that is appropriate for the target journal, filling an important niche for an accessible, open-source spectral-bin microphysics model implemented in a modern computing language. The manuscript provides relevant use cases, validation experiments, and demonstrations of the code's capabilities, and the software should be of interest to a broad community of researchers working in cloud microphysics and precipitation modeling.
The instructions for setting up and running the simulations were clear, and configuring the computational environment was straightforward. The figures were reproduced without difficulty and were consistent with those presented in the paper.
I recommend publication after the following suggestions are addressed.
Specific Comments
The parameter sbin appears throughout the manuscript but is not clearly defined upon first use. Readers unfamiliar with the author's notation must infer its meaning from the equation in line 197 and the subsequent discussion. Please provide an explicit definition (e.g., "number of bins per mass doubling") when first introduced.
L183: More detail on the parallelization strategy would be helpful. Since multiple collision pairs may access the same source and destination bins, it is not immediately clear how race conditions are avoided. Are collision rates evaluated from a frozen particle distribution at the beginning of the timestep, or are bin populations updated during the collision loop? A brief description of the strategy would help readers assess the scalability of the implementation.
L316–324: The manuscript describes the use of multiple particle categories and destination-category routing through “cc_dest” and “br_dest”. However, I think the paper would benefit from expanding on this, as well as an earlier description of how the kernels or efficiencies may change between classes. Please clarify whether kernels (or probably more importantly the associated efficiencies) are evaluated specifically for each category pair, and if all category pairs are allowed to interact. The flexibiIity is evident, but I think some recommendations for specifying these interactions would also be helpful, or at least a discussion of the default behavior when category-pair-specific information is not provided.
Eq. 18 Specify if Dx needs to be the larger size.
L106: Since the manuscript highlights the advantages of a Python implementation and this paragraph details the recent trend towards accessible languages, I suggest including citations of other Python-based microphysics frameworks that have coagulation, including Particula (https://github.com/Gorkowski/particula) and PySDM (https://github.com/open-atmos/PySDM). While these models use a Lagrangian rather than spectral-bin approach, they provide useful context for readers interested in open-source Python tools for cloud microphysics. It is also worth noting the language of LCM1d in L110.
While the repository includes tests for initialization, conservation, and basic diagnostics, the automated test suite appears to contain relatively few tests tied directly to the scientific validation presented in the manuscript. Given the emphasis on analytical benchmark solutions, breakup parameterizations, radar diagnostics, and multi-category interactions, I encourage the authors to consider incorporating more tests based on these published validation cases.
Citation: https://doi.org/10.5194/egusphere-2026-2004-RC1 - AC1: 'Reply on RC1', Edwin Dunnavan, 08 Jul 2026
-
RC2: 'Comment on egusphere-2026-2004', Anonymous Referee #2, 11 Jun 2026
## Summary
This paper presents BinMod1D, a Python package for simulating collisional coalescence and breakup processes in precipitation, with corresponding polarimetric radar signatures. The model can run as a box model, 1D steady-state, or full time/height column model.
Overall Assessment: The paper is well-written and presents valuable tools for the cloud microphysics and radar meteorology community. The work is solid and the implementation is useful. Future work demonstrating specific applications and case studies will help the community better understand where this modeling approach fits within the broader landscape of microphysical modeling tools and how it can generate the most scientific impact.
---
## Comments
General Comment on Figures 1-3: The text describing the methodology is quite dense, and while the figure annotations are good, the narrative could be expanded to better guide readers through interpreting these complex diagrams. Consider adding:
- Step-by-step walkthrough of what each panel shows
- More explicit connections between figure elements and the mathematical terms in equations
- A "how to read this figure" text, especially for Figure 1's complex gain/loss regions
- Clearer transitions from figure to text and backThe figures contain important conceptual information about the numerical scheme, but readers may struggle to extract insights without more narrative scaffolding.
### Discussion
The paper presents a solid technical contribution, but would benefit from expanded discussion of:
- Where BinMod1D fits within the existing ecosystem of microphysical modeling tools
- Specific use cases where this approach has advantages over alternatives.
- Potential applications in operational radar retrieval development or research workflows
- Future work demonstrating community impact through real-world case studiesThis would help readers understand not just *what* the tool does, but *when* and *why* they should use it.
---
## Minor Comments
- Line 127: Missing text - should read "model, due to" (I think)
---
Citation: https://doi.org/10.5194/egusphere-2026-2004-RC2 - AC2: 'Reply on RC2', Edwin Dunnavan, 08 Jul 2026
Peer review completion
Journal article(s) based on this preprint
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Edwin Lee Dunnavan
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4967 KB) - Metadata XML
General Comments
This manuscript presents BinMod1d, a package for atmospheric coagulation and breakup using spectral methods. The paper is clearly written and the figures are intuitive. Overall, this is a useful software contribution that is appropriate for the target journal, filling an important niche for an accessible, open-source spectral-bin microphysics model implemented in a modern computing language. The manuscript provides relevant use cases, validation experiments, and demonstrations of the code's capabilities, and the software should be of interest to a broad community of researchers working in cloud microphysics and precipitation modeling.
The instructions for setting up and running the simulations were clear, and configuring the computational environment was straightforward. The figures were reproduced without difficulty and were consistent with those presented in the paper.
I recommend publication after the following suggestions are addressed.
Specific Comments
The parameter sbin appears throughout the manuscript but is not clearly defined upon first use. Readers unfamiliar with the author's notation must infer its meaning from the equation in line 197 and the subsequent discussion. Please provide an explicit definition (e.g., "number of bins per mass doubling") when first introduced.
L183: More detail on the parallelization strategy would be helpful. Since multiple collision pairs may access the same source and destination bins, it is not immediately clear how race conditions are avoided. Are collision rates evaluated from a frozen particle distribution at the beginning of the timestep, or are bin populations updated during the collision loop? A brief description of the strategy would help readers assess the scalability of the implementation.
L316–324: The manuscript describes the use of multiple particle categories and destination-category routing through “cc_dest” and “br_dest”. However, I think the paper would benefit from expanding on this, as well as an earlier description of how the kernels or efficiencies may change between classes. Please clarify whether kernels (or probably more importantly the associated efficiencies) are evaluated specifically for each category pair, and if all category pairs are allowed to interact. The flexibiIity is evident, but I think some recommendations for specifying these interactions would also be helpful, or at least a discussion of the default behavior when category-pair-specific information is not provided.
Eq. 18 Specify if Dx needs to be the larger size.
L106: Since the manuscript highlights the advantages of a Python implementation and this paragraph details the recent trend towards accessible languages, I suggest including citations of other Python-based microphysics frameworks that have coagulation, including Particula (https://github.com/Gorkowski/particula) and PySDM (https://github.com/open-atmos/PySDM). While these models use a Lagrangian rather than spectral-bin approach, they provide useful context for readers interested in open-source Python tools for cloud microphysics. It is also worth noting the language of LCM1d in L110.
While the repository includes tests for initialization, conservation, and basic diagnostics, the automated test suite appears to contain relatively few tests tied directly to the scientific validation presented in the manuscript. Given the emphasis on analytical benchmark solutions, breakup parameterizations, radar diagnostics, and multi-category interactions, I encourage the authors to consider incorporating more tests based on these published validation cases.