Reading, England – A Year 12 student’s two-week research placement at the National Centre for Atmospheric Science (NCAS) resulted in three new Python “recipes” – short code examples designed to boost coding efficiency for atmospheric scientists.
Coding Skills Meet Climate Data
The placement offered valuable real-world experience and a chance to contribute to open-source scientific tools.
- Oliver Kotla developed Python recipes to help scientists visualize and interpret climate data.
- The recipes are now part of a code library accessible to atmospheric researchers.
- Mentorship played a key role, with both student and scientist learning from each other.
- The experience highlighted the importance of placements for aspiring scientists.
Oliver Kotla, participating in the STEM Learning UK research placement and experiences scheme, worked alongside computational scientist Sadie Bartholomew at NCAS and the University of Reading. The focus was on creating these Python recipes, which streamline common coding tasks and data interpretation, regardless of how the data is stored.
What are the benefits of offering research placements to students? According to Bartholomew, these opportunities provide access to advanced resources and expertise not typically found in school settings. “You get access to much better resources than in the school lab/classroom – vastly more sophisticated equipment to play around with!” she explained.
The software libraries cf-python and cf-plot, maintained by a team at NCAS-CMS in Reading, inspired the project. Bartholomew explained, “Over the past few years we’ve initiated a section of short code scripts we call ‘recipes’ which are designed to demonstrate how the libraries can be used in practice.” These recipes cover tasks like regridding data, plotting temperature anomalies, and visualizing warming stripes.
The collaboration involved a balance of independent study and regular check-ins. Bartholomew encouraged Kotla to explore the libraries and choose topics of interest, providing guidance along the way. “In mentoring and teaching others you tend to learn a lot yourself,” she noted, adding that Kotla also introduced her to new tools for note-taking and coding.
Kotla’s contributions weren’t limited to simply adding recipes. While working on a recipe to plot a wind rose, he identified and fixed a bug in cf-python’s unit conversion function. “I got to work writing and testing a patch for the operation which I was then able to push to cf-python’s source code,” Kotla said. “It feels great being able to make a helpful – albeit small – contribution to a huge project like that!”
The placement also offered unexpected learning opportunities. Kotla was surprised to discover that the JULES land-surface model, used by the Met Office, was written in Fortran, prompting him to begin learning the language himself.
Bartholomew, reflecting on her own experience with a Nuffield Research Placement during her youth, emphasized the value of these opportunities in shaping career paths. “I saw how being embedded in a real research environment allows you to appreciate that a career in science…is an exciting and accessible career and visualise yourself there,” she said. She added that Kotla was her third summer student, and she hopes he felt the same way.
Kotla presented his completed recipes to Bartholomew for feedback before they were added to the public gallery, providing a valuable resource for other scientists using cf-python and cf-plot.
