Spatio-temporal models of west Pacific tropical cyclones
Creators: name: Leahy, Thomas Patrick
Publisher: name: Imperial College London
Year: 2019

description: 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.