Fig: global snowfall map, see code.
Julia offers a fresh approach to scientific computing, high-performance computing and data crunching. Recently designed from the ground up Julia avoids many of the weak points of older, widely used programming languages in science such as Python, Matlab, and R. Julia is an interactive scripting language, yet it executes with similar speed as C(++) and Fortran. Its qualities make it an appealing tool for the geo-scientist.
Julia has been gaining traction in the geosciences over the last years in applications ranging from high performance simulations, data processing, geostatistics, machine learning, differentiable programming to general modelling. The Julia package ecosystem necessary for geosciences has substantially matured, which makes it readily usable for research.
This course provides a show case of some cool geo-applications and a hands-on introduction to get you started with Julia. We aim to give a broad overview of Julia, applications in geoscience, its ecosystem as well as going through hands-on coding exercises. Topics are:
- Why Julia is an interesting language for science
- Showcase of cool geoscience applications
- Learn the basics of Julia to get you started in a hands-on session
Organizers: Mauro Werder, Ludovic Räss, Lazaro Alonso, Simone Silvestri, Milan Kloewer
- Introduction presentation about Julia (slides)
- Showcases of:
- Visualisation of Geo-Data using the plotting library Makie.jl and various geodata libraries; see visualization-of-geodata
- Global ocean modelling with Oceananigans.jl on CPUs and GPUs; see global-ocean-notebooks
- Interactive atmospheric modelling with SpeedyWeather.jl; see speedyweather
- Inverse ice flow modelling and data integration using automatic differentiation on GPUs with Glaide.jl; see glacier-inverse-modelling
- Hands-on exercise to get you started with Julia (see below)
- If you have some Julia experience you can also dive into the notebooks of above showcases
Colab-links to open notebooks in your colab-instance:
- Introduction to Julia basics: julia-basics.ipynb
- Visualisation: vis-geodata.ipynb (install and pre-compile ~10min)
- Atmospheric modelling speedyweather_demo_egu.ipynb (install and pre-compile ~6min)
- Ocean modelling
- global_ocean_simulation_cpu.ipynb (install and pre-compile ~20min)
- global_ocean_simulation_gpu.ipynb (uste T4 GPU instance, install and pre-compile ~20min)
- Ice flow modelling and inversions glacier_inverse_model.ipynb (use T4 GPU instance, install and pre-compile ~30min)
Note
Detailed instructions to open the hands-on notebook julia-basics.jl in Colab with
- https://Colab.research.google.com/github/mauro3/EGU2025-Julia-intro-and-showcase-for-geoscience/blob/main/intro-notebooks/julia-basics.ipynb
- save an editable personal copy; in the menu "File" -> "Save a copy in Drive"
- open the personal copy
- in the menu "Runtime" -> "Change runtime type" -> "Julia" (
⚠️ leaving it at "Julia v1.11.5" does not work) - there is also a solution notebook with all the missing bits filled in: Colab link
The showcase notebooks can be opened with:
- Google Colab
- VScode with Julia kernel for notebook support
- JupyterHub or server that supports Julia kernel, using e.g. IJulia
Warning
Make sure to edit a personal copy of the Colab notebook ("File" -> "Save a copy in Drive") and to select the Julia runtime (instead of Julia 1.11.5); "Runtime" -> "Change runtime type" -> "Julia").
Note
For some of the notebooks GPU acceleration is useful, for this select the "T4 GPU" option.
- Install Julia (probably version 1.11 or the long term stable 1.10): for all operating systems it is best is to use "juliaup": https://github.com/JuliaLang/juliaup
- To install the Julia kernel for Jupyter, follow https://github.com/JuliaLang/IJulia.jl
- Clone or download this repository
- Start Jupyter from within one of the notebook folders such that it picks up the installed packages (defined in
Project.toml). You may need to runusing Pkg; Pkg.update()in the notebook to get the packages actually installed.