Tropical cyclones are devastating destructive forces of nature that can cause loss of life and
catastrophic damage. Understanding the factors that affect tropical cyclone genesis and track
and having accurate models to simulate them is of great interest to the financial and meteorological industries, as well as governmental agencies.
This thesis develops the area of statistical-dynamic models for tropical cyclone genesis and track.
Firstly, we describe the potential predictors of genesis and track independently, marginally
against genesis and track respectively and jointly for genesis. This provides insight into the
ranges of the predictors that are required for genesis.
There are a variety of approaches to modelling genesis and track in the literature, however,
due to a lack of standard quantitative measures, comparatively assessing the performance of
these models is difficult. In this thesis, we develop a range of parametric and non-parametric
models as well as spatial and spatio-temporal models for both genesis and track. For one of the
approaches, we use a Bayesian conditional logistic regression model for genesis which has not
been previously used before and performs well. The Bayesian approach results in a significant
reduction on the bias between the observed and simulated mean annual frequency out-of-sample.
As an example, there is a 94% reduction in the bias between a Bayesian model and its frequentist
counterpart.
A Bayesian approach to modelling the track of a tropical cyclone has not been introduced in
the literature and we develop a Bayesian model for the track that outperforms other models
particularly out-of-sample. Using the main characteristics of tropical cyclone genesis and track,
we develop a set of metrics in which we compare our models to each other and to observation.
Such specific characteristics are the mean annual frequency, the spatial distribution and the
average track. This approach provides transparency and insight into the various modelling
approaches which are fragmented in the literature. It also allows models to be quantitatively
assessed to expose their weaknesses and strengths respectively. For example, we observe a
reduction in the bias of landfall in South East China by more than 76% using the Bayesian
track model developed in this thesis compared a resampling model in the literature.
We apply our models to a likely sea surface temperature scenario that is expected to occur under
climate change. We assess the potential impact on the change of genesis and landfall. We also
propose an alternative two-layer modelling approach to overcome a key shortcoming in current
genesis models, that is obtaining the year-to-year fluctuations in tropical cyclone frequency.
New potential predictors of the frequency of events in the upcoming tropical cyclone season
are defined, with correlations in the region of 0.6 and a model with out-of-sample correlation
of 0.67 with the annual frequency. We describe the larger-scale phenomenon including the El
Nino Modoki pattern that influences the frequency of genesis.