cyclone

<p>Dataset constructed using the International Best Track Archive for Climate Stewardship (IBTrACS).
The distribution of tropical cyclones lifetime maximum intensity across the globe is known
to be bimodal which has important implications for climate modelling.</p>

datasets

A two-step Bayesian approach for mode inference following
Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>).
First, a mixture distribution is fitted on the data using a sparse finite
mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of
mixture components does not have to be known; the size of the mixture is
estimated endogenously through the SFM approach. Second, the modes of the
estimated mixture at each MCMC draw are retrieved using algorithms
specifically tailored for mode detection. These estimates are then used to
construct posterior probabilities for the number of modes, their locations
and uncertainties, providing a powerful tool for mode inference.

Paul Labonne

BayesMultiMode

Bayesian Mode Inference

Nalan Baştürk

Jamie Cross

Peter de Knijff

Lennart Hoogerheide

Herman van Dijk

cyclone function

<p>A dataset with three columns showing the identification of the cyclone, its year of occurrence and its lifetime maximum intensity (LMI).
LMI is calculated as the maximum wind speed for each cyclone with unit ks.</p>

Format

Tropical cyclones lifetime maximum intensity — cyclone

Tropical cyclones lifetime maximum intensity

cyclone: Tropical cyclones lifetime maximum intensity

Description

Usage

Arguments

Format

References