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Published in Geophysical Research Letters (GRL), 2021
Outcome of my master thesis: An investigation of the role of relative humidity for climate sensitivity with a 1D-RCE model.
Recommended citation: Bourdin, Stella; Kluft, Lukas; Stevens, Bjorn. (2021). "Dependence of climate sensitivity on the given distribution of relative humidity." Geophysical Research Letters (GRL). 1(1).
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Published in Geoscientific Model Development (GMD), 2022
Outcome of my PhD thesis: A methodological paper on tropical cyclone tracking.
Recommended citation: Bourdin, S.; Fromang, S.; Dulac, W.; Cattiaux, J.; Chauvin, F. (2022). "Intercomparison of four algorithms for detecting tropical cyclones using ERA5." Geoscientific Model Development (GMD). 1(2).
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Published in Climate Dynamics (CD), 2024
Outcome of my PhD: Assessment of Tropical Cyclones representation in a series of simulations with the IPSL model at high resolution.
Recommended citation: Bourdin, S.; Fromang, S.; Caubel, A.; Ghattas, J.; Meurdesoif, Y.; Dubos, T. (2024). "Tropical Cyclones in Global High-Resolution Simulations using the IPSL Model." Climate Dynamics (CD). 1(3).
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One-week project-based engineering course, CentraleSupélec, Enseignement d'intégration 'Energie & Climat', 2023
I taught this course for three years, and the last two I was in charge. This is a one-week project-based course in which we taught students to run a climate model and interpret different CO2 experiments. They had to choose a specific regional question, and we accompanied them in running and analysing the simulations.
Bloc thématique (TP), CentraleSupélec, Enseignement de mécanique des fluides, 2023
At the request of CentraleSupelec’s Fluid Mechanics professor, I created this 9h (3x3h) practical on Atmospheric Circulation. It consists in teaching the students how to run a Atmospheric Global Climate Model (LMDZ), and letting them create experiments to answer their own atmospheric circulation questions.
Doctoral course, University of Oxford, Intelligent Earth Center for Doctoral Training, 2024
Along with two colleagues, we have created a one-day hands-on course on Environmental Data Analysis. It aims to teach students the main Python packages used in environmental data analysis, while also giving them to keys to solving their own coding problems. It focuses includes: pandas for text and csv files; xarray for netcdf files and satellite data; matplotlib and cartopy for plotting maps. The course is based on open-source notebooks which can be found here.