The Tropical Cyclone Risk Model

The Tropical Cyclone Risk Model is a stochastic tropical cyclone model developed by Geoscience Australia <http://www.ga.gov.au>_ for estimating the wind hazard from tropical cyclones.

Due to the relatively short record of quality-controlled, consistent tropical cyclone observations, it is difficult to estimate average recurrence interval wind speeds ue to tropical cyclones. To overcome the restriction of observed data, TCRM uses an autoregressive model to generate thousands of years of events that are statistically similar to the historical record. To translate these events to estimated wind speeds, TCRM applies a parametric windfield and boundary layer model to each event. Finally an extreme value distribution is fitted to the aggregated windfields at each grid point in the model domain to provide ARI wind speed estimates.

Features

• Multi-platform: TCRM can run on desktop machines through to massively-parallel systems (tested on Windows XP/Vista/7, *NIX);
• Multiple options for wind field & boundary layer models: A number of radial profiles and simple boundary layer models have been included to allow users to test sensitivity to these options.
• Globally applicable: Users can set up a domain in any TC basin in the globe. The model is not tuned to any one region of the globe. Rather, the model is designed to draw sufficient information from best-track archives;
• Evaluation metrics: Offers capability to run objective evaluation of track model metrics (e.g. landfall rates);
• Single scenarios: Users can run a single TC event (e.g. using a b-deck format track file) at high temporal resolution and extract time series data at chosen locations;

Changelog

New features:

• Added empirical ARI calculation

Bug fixes:

• Correction in landfall decay model for unit conversions

Dependencies

TCRM requires:

• Python 3.7 <https://www.python.org/>_;
• numpy <http://www.numpy.org/>_;
• scipy <http://www.scipy.org/>_;
• matplotlib <http://matplotlib.org/>_;
• Basemap <http://matplotlib.org/basemap/index.html>_;
• netcdf4-python <https://unidata.github.io/netcdf4-python/netCDF4/index.html>_;
• cftime <https://unidata.github.io/cftime/>_;
• pandas <http://pandas.pydata.org/>_;
• Shapely <https://shapely.readthedocs.io/en/latest/manual.html>_;
• seaborn <https://seaborn.pydata.org/>_;
• statsmodels <http://statsmodels.sourceforge.net>_;
• GitPython <http://gitpython.readthedocs.io>_;
• GDAL/OGR <https://pypi.org/project/GDAL/>_;
• mpi4py <https://mpi4py.readthedocs.io/en/stable/>_;
• and gcc.

Status

.. image:: https://github.com/GeoscienceAustralia/tcrm/actions/workflows/tcrm-tests.yml/badge.svg?branch=master :target: https://github.com/GeoscienceAustralia/tcrm/actions/workflows/tcrm-tests.yml :alt: Build status

.. image:: https://coveralls.io/repos/GeoscienceAustralia/tcrm/badge.svg?branch=master :target: https://coveralls.io/r/GeoscienceAustralia/tcrm?branch=master :alt: Test coverage

.. image:: https://landscape.io/github/GeoscienceAustralia/tcrm/master/landscape.svg?style=flat :target: https://landscape.io/github/GeoscienceAustralia/tcrm/master :alt: Code Health

.. image:: https://zenodo.org/badge/10637300.svg :target: https://zenodo.org/badge/latestdoi/10637300

Screenshot

.. image:: docs/screenshot.png

Contributing to TCRM

If you would like to take part in TCRM development, take a look at the Contributing guide <docs/contributing.rst>_.

License

This repository is licensed under the GNU General Public License. See the file LICENSE.rst <LICENSE.rst>_ for information on the history of this software, terms and conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.

Contacts

Community Safety Branch Geoscience Australia hazards@ga.gov.au