Jamie Kettleborough
Climate Research Data Applications
Greater Exeter Area United Kingdom
Summary
👤
Senior🎓
Top SchoolJamie Kettleborough is a climate research data applications specialist with 13 years of experience applying scientific software practices to atmospheric and Earth-system science at the Met Office. He combines a PhD in Atmospheric Chemistry with a long track record in data science and postdoctoral research to deliver robust, test-driven Python tools for climate analysis. His open-source contributions to the widely used SciTools/iris project show a practical focus on coordinate correctness, performance (NumPy-optimized cell-area computations), and clearer documentation. Colleagues rely on him for lightweight use-case analysis that turns complex geospatial data issues into reliable, maintainable code. Based in the Greater Exeter area, he brings deep domain knowledge and an engineer’s attention to testing and reproducibility.
13 years of coding experience
6 years of employment as a software developer
BsC, Chemical Physics, BsC, Chemical Physics at The University of EdinburghUniversity of Cambridge
Github Skills (9)
python10
cartography10
numpy10
data-analysis10
testing9
data-structure9
data-structures9
visualizations5
visualization5
Programming languages (2)
Github contributions (5)
SciTools/iris
Nov 2013 - Oct 2015
A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 6 PRs, 27 comments in 1 year 10 months
Contributions summary:Jamie's contributions center on improving the Iris library's data analysis capabilities, particularly concerning coordinate systems and geographic data. They made several changes related to bounds handling in the `DimCoord` class, including implementing tests to ensure correctness. Furthermore, the user optimized the calculation of cell areas within the cartography module using NumPy arrays and improved documentation. These modifications suggest a focus on enhancing the library's data processing and analytical performance.
pythonvisualisationagnosticgribatmosphere
jkettleb/iris
Jul 2015 - Jan 2019
A powerful, easy to use, and community-driven Python library for analysing and visualising meteorological and oceanographic data sets.
Contributions:12 pushes, 20 branches in 3 years 6 months
python-librarymeteorologypythondata-setsrisk-assessment
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