Evaluating the likelihood and magnitude of tropical cyclone winds based on a stochastic TC model (TCRM: https://github.com/geoscienceaustralia/tcrm).
Scripts to analyse the observational records of TCs and automatic weather station observations
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).
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.
lmi/extractLMI.py lmi/extractLMI_IDCKMSTM0S.py
Using the theory of potential intensity to guide estimation of simulated TC intensity.
precip/extract_precip.py - extracts ERA5 precipitation within a defined distance of the cyclone centre.
Craig Arthur craig.arthur@ga.gov.au