https://github.com/geoscienceaustralia/tcha
https://github.com/geoscienceaustralia/tcha
climate hazards probabilistic-models risk tropical-cyclone winds
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/geoscienceaustralia/tcha
- Owner: GeoscienceAustralia
- Created: 2021-09-13T02:04:09.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-17T02:31:35.000Z (about 1 year ago)
- Last Synced: 2025-03-27T13:46:05.458Z (7 months ago)
- Topics: climate, hazards, probabilistic-models, risk, tropical-cyclone, winds
- Language: Jupyter Notebook
- Homepage:
- Size: 12.6 MB
- Stars: 3
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
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README
Tropical Cyclone Hazard Assessment
++++++++++++++++++++++++++++++++++
Evaluating the likelihood and magnitude of tropical cyclone winds based on a
stochastic TC model (TCRM: https://github.com/geoscienceaustralia/tcrm).
Analysis of observations
------------------------
Scripts to analyse the observational records of TCs and automatic weather
station observations
TC frequency
~~~~~~~~~~~~
frequency/tc_frequency.py - calculates mean frequency and trends for a range of
TC datasets and time periods of those datasets.
frequency/jtwc_frequency.py - Uses JTWC data to evaluate frequency
frequency/frequency_distribution.py - fits a negative binomial distribution to
annual frequency, for consideration as the source model for TCRM. Negative
binomial initially selected over poisson distribution, as the distribution is
very slightly overdispersed ([mu / sigma] < 1).
frequency/tc_frequency_bayesian.py - use Bayesian MCMC methods to fit Poisson
distribution to TC frequency, and generate posterior samples that can be used
for sampling annual TC counts.
Track density
~~~~~~~~~~~~~
density/track_density.py - calculates TC frequency on a grid, counting the
number of unique events intersecting each grid point. Currently uses the BoM
best track dataset (IDCKMSTM0S.csv) as input, and a 0.5x0.5 degree grid over the
simulation domain.
Compares 1981-2020 and 1951-2020 periods.
Uses jackknife (leave-one-out) bootstrap resampling to evaluate mean track
density, by iteratively excluding seasons from the dataset for calculating track
density.
To run::
``python density/track_density.py``
TC landfall rates
~~~~~~~~~~~~~~~~~
Lifetime maximum intensity
~~~~~~~~~~~~~~~~~~~~~~~~~~
lmi/extractLMI.py
lmi/extractLMI_IDCKMSTM0S.py
Potential intensity analysis
----------------------------
Using the theory of potential intensity to guide estimation of simulated TC
intensity.
Basin-wide trends
~~~~~~~~~~~~~~~~~
Monthly trends
~~~~~~~~~~~~~~
Potential intensity from climate models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Deep layer mean winds
---------------------
dlm-climatology/climatology.py -
TC-related rainfall
-------------------
precip/extract_precip.py - extracts ERA5 precipitation within a defined distance
of the cyclone centre.
Contact:
--------
Craig Arthur
craig.arthur@ga.gov.au
Last updated: 2023-07-20