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

EarthArXiv preprint available at https://doi.org/10.31223/X57T5R.

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 a 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 (or almost all) of the key experiments are carried out as slurm jobs — see slurm/. data/ contains some of the key data, and img/ most of the key figures. docs/ contains the source for the ReadTheDocs documentation at https://worstsurge.readthedocs.io/en/latest/MAIN_README.html.

Modules

tcpips

Tropical Cyclone Potential Intensity (PI) and precursors for Potential Size (PS) calculations. Includes a pangeo script to download and process CMIP6 data. Regrids using cdo or xESMF. Uses the tcpypi package to calculate potential intensity. Also includes tcpips/ibtracs.py to compare IBTrACS observations with potential sizes and intensities calculated from ERA5 monthly averages (post-1980), with filters for storms undergoing extratropical transition.

w22

Calculates tropical cyclone potential size following Wang et al. (2022). The implementation of Potential Size is in w22/ps.py, which can now calculate both potential sizes together. w22/stats2.py generates tables describing CMIP6 results (for HadGEM3-GC31-MM, MIROC6, and CESM2).

# Calculate example potential sizes
python -m w22.ps
# Run tests against W22
python -m w22.test

cle15

A standalone Python re-implementation of the Chavas, Lin & Emanuel (2015) tropical cyclone wind profile model. Three variants are provided:

Module	Description
cle15.py	Pure-Python reference implementation
cle15n.py	Numba-accelerated drop-in replacement (~10× faster)
cle15m.py	Thin wrapper around the original MATLAB/Octave scripts in mcle/

All three expose the same public API: run_cle15, profile_from_stats, process_inputs. The Numba implementation is now the default in w22.

# Run benchmarks
python -m cle15.bench_cle15
# Run tests
python -m cle15.test_cle15

adforce

An ADCIRC wrapper for forcing and post-processing. Handles idealised axisymmetric tropical cyclones (NWS=13, ADCIRC ≥ 55.02) with a gradient wind reduction factor of $V_\text{reduc} = 0.8$ for 10 m winds. Uses hydra for config management.

Key files:

• fort22.py — write wind/pressure fort.22.nc input files; supports lc12 asymmetry and Ide et al. (2022) curved parabolic tracks.
• fort61.py / fort63.py — read tide gauge and SSH/wind output.
• mesh.py — fast mesh reading/processing; converts ADCIRC output to dual-graph format for GNN training.
• dual_graph.py — dual-graph construction for ML training datasets.
• generate_training_data.py — forces ADCIRC with IBTrACS historical U.S. landfalling storms (1980–2024) to generate SurgeNet training data.
• wrap.py — orchestrates parallel ADCIRC runs on Archer2; the main file to edit when porting to a new machine.

Supports pyproj and a fast sphere approximation for distance calculations (controlled by geoid in adforce/config/grid/grid_fort22.yaml; defaults to sphere).

python -m adforce.wrap

Requires padcirc and adcprep executables placed in adforce/config/files/. A repo with compilation settings and minor edits is available at https://github.com/sdat2/adcirc-v55.02.

adbo

Bayesian optimization using adforce to drive ADCIRC via a trieste optimization loop. Supports multiple GP kernels and acquisition functions. Uses argparse for top-level config management, then delegates to hydra inside adbo.

Key scripts:

• exp_1d.py — 1D Bayesian optimization experiment.
• exp_2d.py / exp_3d.py — 2D and 3D experiments.
• gp_exp.py — exploration of different GP kernels.
• create_test_set.py — builds the potential-height test set (best BO run per location) for SurgeNet training.

python -m adbo.exp_1d --test True --exp_name test

worst

Statistical worst-case GEV fit with scipy (sci.py) and tensorflow (tens.py). Explores the effect of knowing the upper bound ahead of time on sampling uncertainty and bias. Uses hydra for config management.

GEV parameterisation used: location $\alpha$, scale $\beta > 0$, shape $\gamma < 0$ (Weibull), giving upper bound $z^\star = \alpha - \beta/\gamma$.

python -m worst.vary_gamma_beta
python -m worst.vary_samples_ns

surgenet

Early-stage work on a graph neural network (GNN) emulator for storm surge, trained on ADCIRC output. Currently contains a toy example (toy_example.py) demonstrating pathological extrapolation behaviour of ReLU neural networks compared with the true analytical exceedance probability.

Repository layout

adbo/        Bayesian optimization loop (trieste)
adforce/     ADCIRC wrapper and forcing utilities
cle15/       CLE15 wind profile implementations (pure-Python & Numba)
data/        Key input data (fort.22.nc, IBTrACS, ERA5, CMIP6, etc.)
docs/        ReadTheDocs source
img/         Key figures
slurm/       SLURM job scripts for all major experiments
surgenet/    GNN storm surge emulator (early stage)
tcpips/      Potential intensity & size from CMIP6/ERA5
w22/         Wang et al. (2022) potential size calculation
worst/       EVT upper-bound GEV fitting

Getting started

Use conda to create the environment:

conda env create -n tcpips -f env.yml
conda activate tcpips

Or with micromamba (faster):

micromamba create -n tcpips -f env.yml
micromamba activate tcpips

Install the repository in editable mode:

pip install -e .

Citations

If you use this code, please cite the preprint:

@article{potential_height_paper_2025,
  title={Finding the potential height of tropical cyclone storm surges in a changing climate using Bayesian optimization},
  author={Thomas, Simon D. A. and Jones, Dani C. and Mayo, Talea and Taylor, John R. and Moss, Henry B. and Munday, David R. and Haigh, Ivan D. and Gopinathan, Devaraj},
  journal={EarthArXiv (Submitted to Environmental Data Science)},
  year={2025},
  doi={10.31223/X57T5R}
}

And cite the code as:

@software{potential_height_code,
  author = {Thomas, Simon D. A.},
  doi = {10.5281/zenodo.15073504},
  month = {Nov},
  title = {{Finding the potential height of tropical cyclone storm surges in a changing climate using Bayesian Optimization}},
  url = {https://github.com/sdat2/PotentialHeight},
  version = {v0.1.3},
  year = {2025}
}

The CLE15 wind profile model (used via cle15/) should also be cited as:

@software{cle_2015_code,
  title = {Code for tropical cyclone wind profile model of Chavas et al (2015, JAS)},
  month = {Jun},
  url = {https://purr.purdue.edu/publications/4066/1},
  year = {2022},
  doi = {10.4231/CZ4P-D448},
  author = {Daniel Robert Chavas}
}