WorstSurge: Finding the potential height of tropical cyclone storm surges in a changing climate using Bayesian optimization

We want to answer the question of what the potential height of a storm surge could be now and in a changing climate. To do this we first calculate the potential intensity and size from CMIP6 (tcpips & w22), and then use a Bayesian optimization loop (adbo) to drive an storm surge model ADCIRC with idealised tropical cyclones (adforce). We then show that knowing the upper bound can be useful in the context of an evt fit (worst).

All of the key experisments are carried out as slurm jobs, so go to slurm/ to see these. data/ contains some of the key data, and img/ most of the key figures. docs/ contains the source for the readthedocs documentation https://worstsurge.readthedocs.io/en/latest/MAIN_README.html.

tcpips

Tropical Cyclone Potential Intensity (PI) and precursors for Potential Size (PS) calculations. Includes pangeo script to download and process data from CMIP6 for calculations. Currently regrids using cdo or xESMf. Uses the tcpypi pypi package to calculate potential intensity.

w22

Chavas et al. 2015 profile calculation in matlab (using octave instead).

Used to calculate tropical cyclone potential size as in Wang et al. (2022).

adforce

ADCIRC forcing and processing. Some generic mesh processing. Assume gradient wind reduction factor V_reduc=0.8 for 10m wind. Runs using hydra for config management.

python -m adforce.wrap

Needs an executatable directory for ADCIRC that includes a padcirc and adcprep executable stored in adforce/config/files/.

A repo with our compilation settings for ADCIRC, and small edits, is available at https://github.com/sdat2/adcirc-v55.02.

adbo

Bayesian optimization using adforce to force ADCIRC with a trieste Bayesian optimization loop. Runs using argparse for config management, and then calls adbo which uses hydra.

python -m adbo.exp_3d

worst

Statistical worst-case GEV fit using tensorflow. Varies whether to assume an upper bound ahead of time. Uses hydra for the config management.

Getting started

Developer install:

# Use conda to install a python virtual environment
conda env create -n tcpips -f env.yml
conda activate tcpips

# or use micromamba (faster)
# maybe you need to activate micromamba "$(micromamba shell hook --shell zsh)"
micromamba create -n tcpips -f env.yml
micromamba activate tcpips

# Install repository in editable version
pip install -e .