{"id":13949006,"url":"https://github.com/GeoscienceAustralia/tcrm","last_synced_at":"2025-07-20T11:30:32.307Z","repository":{"id":8911951,"uuid":"10637300","full_name":"GeoscienceAustralia/tcrm","owner":"GeoscienceAustralia","description":"A statistical-parametric model for assessing wind hazard from tropical cyclones","archived":false,"fork":false,"pushed_at":"2024-11-13T00:08:03.000Z","size":99700,"stargazers_count":91,"open_issues_count":43,"forks_count":54,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-07-14T04:45:58.023Z","etag":null,"topics":["hazard-assessment","python","risk-assessment","tropical-cyclone","wind"],"latest_commit_sha":null,"homepage":"http://geoscienceaustralia.github.io/tcrm","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GeoscienceAustralia.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":"docs/contributing.rst","funding":null,"license":"LICENSE.rst","code_of_conduct":"CODE_OF_CONDUCT.rst","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2013-06-12T06:03:27.000Z","updated_at":"2025-06-23T00:43:12.000Z","dependencies_parsed_at":"2024-03-17T04:42:39.905Z","dependency_job_id":"80db5bdb-0f96-4843-84cd-b88578d94c0c","html_url":"https://github.com/GeoscienceAustralia/tcrm","commit_stats":{"total_commits":1190,"total_committers":16,"mean_commits":74.375,"dds":0.4445378151260504,"last_synced_commit":"2109cdd9101c1b5d68b705182b8f2352a7b54592"},"previous_names":[],"tags_count":36,"template":false,"template_full_name":null,"purl":"pkg:github/GeoscienceAustralia/tcrm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcrm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcrm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcrm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcrm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GeoscienceAustralia","download_url":"https://codeload.github.com/GeoscienceAustralia/tcrm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcrm/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265491672,"owners_count":23775878,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["hazard-assessment","python","risk-assessment","tropical-cyclone","wind"],"created_at":"2024-08-08T05:01:36.156Z","updated_at":"2025-07-20T11:30:32.264Z","avatar_url":"https://github.com/GeoscienceAustralia.png","language":"Python","funding_links":[],"categories":["Climate Change"],"sub_categories":["Natural Hazard and Storms"],"readme":"The Tropical Cyclone Risk Model\n===============================\n\nThe **Tropical Cyclone Risk Model** is a stochastic tropical cyclone\nmodel developed by `Geoscience Australia \u003chttp://www.ga.gov.au\u003e`_ for estimating the wind hazard from tropical cyclones.\n\nDue to the relatively short record of quality-controlled, consistent\ntropical cyclone observations, it is difficult to estimate average\nrecurrence interval wind speeds ue to tropical cyclones. To overcome\nthe restriction of observed data, TCRM uses an autoregressive model to\ngenerate thousands of years of events that are statistically similar\nto the historical record. To translate these events to estimated wind\nspeeds, TCRM applies a parametric windfield and boundary layer model\nto each event. Finally an extreme value distribution is fitted to the\naggregated windfields at each grid point in the model domain to\nprovide ARI wind speed estimates.\n\n\nFeatures\n========\n* **Multi-platform**: TCRM can run on desktop machines through to massively-parallel systems (tested on Windows XP/Vista/7, \\*NIX);\n* **Multiple options for wind field \u0026 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.\n* **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;\n* **Evaluation metrics**: Offers capability to run objective evaluation of track model metrics (e.g. landfall rates);\n* **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;\n\n\nChangelog\n=========\n\nNew features:\n-------------\n\n* Added empirical ARI calculation\n\n\nBug fixes:\n----------\n\n* Correction in landfall decay model for unit conversions\n\nDependencies\n============\n\nTCRM requires:\n\n * `Python 3.7 \u003chttps://www.python.org/\u003e`_;\n * `numpy \u003chttp://www.numpy.org/\u003e`_; \n * `scipy \u003chttp://www.scipy.org/\u003e`_;\n * `matplotlib \u003chttp://matplotlib.org/\u003e`_; \n * `Basemap \u003chttp://matplotlib.org/basemap/index.html\u003e`_; \n * `netcdf4-python \u003chttps://unidata.github.io/netcdf4-python/netCDF4/index.html\u003e`_; \n * `cftime \u003chttps://unidata.github.io/cftime/\u003e`_;\n * `pandas \u003chttp://pandas.pydata.org/\u003e`_; \n * `Shapely \u003chttps://shapely.readthedocs.io/en/latest/manual.html\u003e`_; \n * `seaborn \u003chttps://seaborn.pydata.org/\u003e`_;\n * `statsmodels \u003chttp://statsmodels.sourceforge.net\u003e`_;\n * `GitPython \u003chttp://gitpython.readthedocs.io\u003e`_;\n * `GDAL/OGR \u003chttps://pypi.org/project/GDAL/\u003e`_;\n * `mpi4py \u003chttps://mpi4py.readthedocs.io/en/stable/\u003e`_;\n * and `gcc`.  \n\n\n\nStatus\n======\n\n\n.. image:: https://github.com/GeoscienceAustralia/tcrm/actions/workflows/tcrm-tests.yml/badge.svg?branch=master\n    :target: https://github.com/GeoscienceAustralia/tcrm/actions/workflows/tcrm-tests.yml\n    :alt: Build status\n\n\n.. image:: https://coveralls.io/repos/GeoscienceAustralia/tcrm/badge.svg?branch=master\n  :target: https://coveralls.io/r/GeoscienceAustralia/tcrm?branch=master\n  :alt: Test coverage\n\n    \n.. image:: https://landscape.io/github/GeoscienceAustralia/tcrm/master/landscape.svg?style=flat\n    :target: https://landscape.io/github/GeoscienceAustralia/tcrm/master\n    :alt: Code Health\n    \n.. image:: https://zenodo.org/badge/10637300.svg\n   :target: https://zenodo.org/badge/latestdoi/10637300\n\nScreenshot\n==========\n\n.. image:: docs/screenshot.png\n\nContributing to TCRM\n====================\n\nIf you would like to take part in TCRM development, take a look at the `Contributing guide \u003cdocs/contributing.rst\u003e`_.\n\nLicense\n=======\n\nThis repository is licensed under the GNU General Public License. See\nthe file `LICENSE.rst \u003cLICENSE.rst\u003e`_\nfor information on the history of this software, terms and conditions\nfor usage, and a DISCLAIMER OF ALL WARRANTIES.\n\nContacts\n========\n\nCommunity Safety Branch\nGeoscience Australia\nhazards@ga.gov.au\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FGeoscienceAustralia%2Ftcrm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FGeoscienceAustralia%2Ftcrm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FGeoscienceAustralia%2Ftcrm/lists"}