{"id":18818935,"url":"https://github.com/geoscienceaustralia/tcha","last_synced_at":"2025-04-13T23:32:55.584Z","repository":{"id":39257910,"uuid":"405802657","full_name":"GeoscienceAustralia/tcha","owner":"GeoscienceAustralia","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-17T02:31:35.000Z","size":13212,"stargazers_count":3,"open_issues_count":0,"forks_count":4,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-27T13:46:05.458Z","etag":null,"topics":["climate","hazards","probabilistic-models","risk","tropical-cyclone","winds"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GeoscienceAustralia.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"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}},"created_at":"2021-09-13T02:04:09.000Z","updated_at":"2024-10-17T02:31:39.000Z","dependencies_parsed_at":"2024-03-22T02:29:32.868Z","dependency_job_id":"48f024d0-06be-4384-918a-eb7fed31ea7a","html_url":"https://github.com/GeoscienceAustralia/tcha","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcha","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcha/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcha/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoscienceAustralia%2Ftcha/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GeoscienceAustralia","download_url":"https://codeload.github.com/GeoscienceAustralia/tcha/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248796954,"owners_count":21163056,"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":["climate","hazards","probabilistic-models","risk","tropical-cyclone","winds"],"created_at":"2024-11-08T00:19:31.223Z","updated_at":"2025-04-13T23:32:50.929Z","avatar_url":"https://github.com/GeoscienceAustralia.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Tropical Cyclone Hazard Assessment\n++++++++++++++++++++++++++++++++++\n\nEvaluating the likelihood and magnitude of tropical cyclone winds based on a\nstochastic TC model (TCRM: https://github.com/geoscienceaustralia/tcrm).\n\n\n\nAnalysis of observations\n------------------------\n\nScripts to analyse the observational records of TCs and automatic weather\nstation observations\n\n\nTC frequency\n~~~~~~~~~~~~\n\nfrequency/tc_frequency.py - calculates mean frequency and trends for a range of\nTC datasets and time periods of those datasets.\nfrequency/jtwc_frequency.py - Uses JTWC data to evaluate frequency\nfrequency/frequency_distribution.py - fits a negative binomial distribution to\nannual frequency, for consideration as the source model for TCRM. Negative\nbinomial initially selected over poisson distribution, as the distribution is\nvery slightly overdispersed ([mu / sigma] \u003c 1).\nfrequency/tc_frequency_bayesian.py - use Bayesian MCMC methods to fit Poisson\ndistribution to TC frequency, and generate posterior samples that can be used\nfor sampling annual TC counts.\n\n\nTrack density\n~~~~~~~~~~~~~\n\ndensity/track_density.py - calculates TC frequency on a grid, counting the\nnumber of unique events intersecting each grid point. Currently uses the BoM\nbest track dataset (IDCKMSTM0S.csv) as input, and a 0.5x0.5 degree grid over the\nsimulation domain.\n\nCompares 1981-2020 and 1951-2020 periods.\n\nUses jackknife (leave-one-out) bootstrap resampling to evaluate mean track\ndensity, by iteratively excluding seasons from the dataset for calculating track\ndensity.\n\nTo run::\n\n    ``python density/track_density.py``\n\n\nTC landfall rates\n~~~~~~~~~~~~~~~~~\n\n\nLifetime maximum intensity\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nlmi/extractLMI.py\nlmi/extractLMI_IDCKMSTM0S.py\n\n\nPotential intensity analysis\n----------------------------\n\nUsing the theory of potential intensity to guide estimation of simulated TC\nintensity.\n\n\nBasin-wide trends\n~~~~~~~~~~~~~~~~~\n\nMonthly trends\n~~~~~~~~~~~~~~\n\n\nPotential intensity from climate models\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n\nDeep layer mean winds\n---------------------\ndlm-climatology/climatology.py -\n\nTC-related rainfall\n-------------------\nprecip/extract_precip.py - extracts ERA5 precipitation within a defined distance\nof the cyclone centre.\n\nContact:\n--------\n\nCraig Arthur\ncraig.arthur@ga.gov.au\nLast updated: 2023-07-20","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeoscienceaustralia%2Ftcha","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeoscienceaustralia%2Ftcha","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeoscienceaustralia%2Ftcha/lists"}