Dear authors of the manuscript "Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observation Simulator", I had the pleasure to read your manuscript describing a tool that undoubtedly will prove itself to be a very useful and important to the validation and quality assessment of our climate models. However, I do have a few minor comments about this paper

1. On page 3, line 72 you speculate that this software could be a valuable tool for upcoming CMIP7 experiments. However, in the rest of the text, I get the impression that VISION is designed to work with hourly output from UM/UKESM with specific output file names. Furthermore, to my knowledge the most frequent global fields in CMIP are 3-hourly. Does the tool operate on CMIP6 CMORized output? If not, could you elaborate in the discussion what would be needed for VISION to claim a role in the CMIP7 assessment cycle?
2. In section 2.3 you briefly discuss the output of the VISION tool. Is the output CF-compliant? Please mention if so.
3. Table 2: you test different I/O libraries for NetCDF on performance. Surprisingly, the cf-python library is faster when reading a pp file with 36 fields than a single field. Could you elaborate on this? In a broader perspective, I'm not sure whether a numpy.print is a good indicator for the I/O performance of the actual VISION workload, especially for distributed lazy I/O libraries under consideration. Maybe extracting values along a trajectory would provide a better indication of the performance? Please address this concern in the text.
4. Section 4: the examples only involve ozone concentration. Since the tool is presented as a general-purpose interpolator/collocator, one would expect multiple variables to be plotted for illustration.

(1) I believe that it would be worth mentioning the following tangential point.

Another scenario where the overall approach described can be beneficial is for the creation of Nature Run (NR) simulations used for OSSEs.   These will generally be much higher in resolution than climate simulations, and therefore allow for an even higher compression ratio.  Further, practical limitations generally limit NR output to infrequent snapshots, whereas there is great research value in sampling at the model time step.    However, to be practical, producing in-situ data in this configuration will generally require online processing to avoid the costly intermediate step of dumping full states to disk as the first step in the processing workflow.    OTOH, online processing would allow for improved scalability of the interpolation step.  It would be nice if future versions of VISION would allow for an online distributed interface for such scenarios.

(2) To a limited degree a similar approach has been used for field campaigns.  E.g.,  https://github.com/GEOS-ESM/GMAOpyobs/blob/develop/src/pyobs/sampler.py

The paper “Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator” by Maria R. Russo et al. addresses an important scientific question: how can we accurately and effectively compare climate model output and observational data? The authors achieve this by co-locating model output to the same time and location as the corresponding observations, which is a very reasonable approach. This does not only reduce the amount of data that needs to be stored, but also allows a direct comparison of model output and observations.

The paper is well-structured and fits well into the scope of the journal. I really enjoyed reading it! Thus, I only have some minor comments which I hope can be used to improve the manuscript even further.

General Comments

• Regarding section 2.4 “Code optimization”: Could you give a rough estimate of how much real time running ISO_simulator takes? Are you using any of the parallel/distributed/out-of-core computing functionalities provided by cf-python or Iris via Dask? This could potentially lead to large performance improvements.
• Please consider publishing your code as a Python package on PyPI and/or conda-forge to enable installing it via “pip install ” or “conda install ”. This will greatly simplify the installation process, the dependency handling, and the inclusion of your code into other software. For example, other software products could simply do a “import <name_of_your_package” in their code.

Specific Comments

1. 23: I think it would be helpful to also include the acronym “ISO_simulator” into the title. You mention it very often in the paper, so I think it deserves to be there.
2. 45: There are many models which also use unstructured grids (ICON, FESOM, etc.), so it’s probably better to avoid the term “regular grid”, which really is the opposite of an unstructured (or irregular) grid.
3. 83-86: Mention what you need Iris for? Both other tools are mentioned here.
4. Table 1: Please mention that input files can also be other formats than PP (like you do in the next paragraph).
5. Table 2: Could you please explain what you mean by “Iris + structured UM loading” and why the difference is so big between “Iris” and that?
6. 143: Iris 3.1.0 is very old (Sep. 2021), have you considered using a later version?
7. 189-191: You already mentioned a lot of this in the paragraph l.180-185, maybe you can unify this?

Technical Corrections

1. 35: “NERC” is not defined
2. 114-115: “input variable” -> “command line argument”
3. 188: “UAV” is undefined