This project assesses how different tropical cyclone tracking algorithms influence estimates of cyclone behaviour by benchmarking automated methods against IBTrACS and CMIP6 data to reduce uncertainty in Australia’s future climate risk projections.
How we "see" a tropical cyclone in a climate model depends entirely on the digital lens we use to track it. Different tracking algorithms (e.g., pressure-based vs. vorticity-based) can produce vastly different results for cyclone frequency, duration, and landfall intensity. This "tracking uncertainty" creates a significant bottleneck in our ability to provide reliable risk assessments for Australia’s future.
This PhD project will systematically evaluate how tracking methodologies shape our understanding of tropical cyclone (TC) behavior. The candidate will compare state-of-the-art automated trackers against the International Best Track Archive (IBTrACS) and apply these methods to CMIP6 datasets. The goal is to establish a robust "benchmark" for tracking that remains consistent from the open ocean to post-landfall, ultimately reducing the uncertainty in future climate projections.
School of Science, UNSW Canberra
Extreme weather and climate
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