Data-driven discovery of Spatiotemporal Causal Precursors to Tropical Weather Extremes
|Iat hin TAM
|Director of thesis
|Co-director of thesis
|Summary of thesis
This proposed thesis aims to understand how data-driven statistical modelling and machine learning (ML)
can yield new physical insights into what causes tropical weather extremes. All proposed projects in this
thesis will focus on tropical cyclones (TCs). In the three projects proposed in this thesis, we will explore
utilizing different data-driven methodologies to address two questions regarding TCs that improve the slowest
in operational forecasts: the genesis of TCs and extreme precipitation associated with TCs of different
The overarching objective of this thesis is to better understand the causal pathways that (I) cause a few
tropical cloud clusters to undergo a sudden regime change from regular mesoscale convective systems (MCSs)
to intensifying tropical cyclones, and (II) cause a TC to produce more heavy and sustained precipitation.
What combination of processes or chain of events results in the greatest likelihood of these destructive
phenomena? That is what we hope to clarify in this thesis.
|Administrative delay for the defence