JuliaCon 2020 (times are in UTC)

BITE, a Bayesian glacier thickness estimation model
07-31, 13:10–13:20 (UTC), Purple Track

BITE is a new glacier thickness estimation model based on a mass-conserving forward model and a Bayesian inversion scheme. The model is fitted to available data using a Markov chain Monte Carlo (MCMC) method. The model is applied to more than 30,000 glaciers representing about 1/6 of the total. Thanks to Julia's speed it was possible to calculate the 1e8 glacier thickness maps necessary for the MCMC procedure.


Accurate estimations of glacier ice thickness and volume are indispensable for ice flow modelling, hydrological forecasts and sea-level rise projections. I present a new ice thickness estimation model [1,2] based on a mass-conserving forward model and a Bayesian inversion scheme (BITE.jl code on github [3]). The model assimilates observations of ice thickness and speed using a Bayesian scheme implemented with a Markov chain Monte Carlo method, which calculates estimates of ice thickness and their error. The model is validated using 733 glaciers from four regions of the world with ice thickness measurements, and it is demonstrated that the model can be used for large-scale studies by fitting it to over 30,000 glaciers from around the globe. I will detail how Julia's speed as well as its productivity was indispensable in making the project succeed.

[1] https://doi.org/10.1017/jog.2019.93,
[2] https://juliacomputing.com/case-studies/bayesian.html,
[3] https://github.com/mauro3/BITEmodel.jl

Glaciologist at ETH Zurich, Switzerland