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.

## 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 back

The 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 studies

This would help readers understand not just *what* the tool does, but *when* and *why* they should use it.

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## Minor Comments

- Line 127: Missing text - should read "model, due to" (I think)

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