Peer-Review Feedback

[vddesai-97]

This issue is for collecting peer-review feedback on M2-Computational-Climate-Science. Please share your thoughts on the strengths and weaknesses of the module. Feel free to provide specific examples, suggestions, or issues you encountered while reviewing.

[vddesai-97]

This module covers the use of XArray to work with multi-dimensional data cubes, demonstrating how to slice data spatially and temporally. It provides an excellent introduction to handling large or multiple NetCDF files using xr.open_mfdataset(). Additionally, it clearly explains how to manage coordinate systems in lat/lon geospatial datasets in an accessible way.

Since this is a NASA-funded project, there is a strong emphasis on NASA datasets, which is great. However, introducing users to non-NASA datasets is also valuable. I appreciate the inclusion of CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations, a dataset developed by USGS and UC Santa Barbara. Expanding this approach across more modules could be beneficial, as there are many other open data platforms worth exploring. Demonstrating how to access and utilize a variety of those datasets would broaden the scope and applicability of these modules.

I also like that some notebooks include embedded challenges encouraging users to apply what they’ve learned. Expanding this by incorporating more challenges throughout, and even adding a final challenge at the end of each module, would give students a structured way to reinforce their understanding. This would allow them to build confidence in debugging and problem-solving before they attempt independent analyses where unexpected issues are more likely to arise.