TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones
Lin Yan, Hanqi Guo, Thomas Peterka, Bei Wang, Jiali Wang

TL;DR
TROPHY is a physics-informed, topologically robust framework that enhances tropical cyclone tracking efficiency using 2D wind data, outperforming traditional methods in large-scale climate datasets.
Contribution
The paper introduces TROPHY, a novel physics-informed tracking framework that significantly improves computational efficiency and accuracy in tropical cyclone detection using only wind vector fields.
Findings
TROPHY accurately tracks TCs over 30 years of reanalysis data.
It filters out 90% of short-lived critical points during preprocessing.
The framework outperforms existing algorithms in capturing TC characteristics.
Abstract
Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. This paper introduces a…
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Taxonomy
TopicsOpportunistic and Delay-Tolerant Networks
MethodsFeature Selection · Focus
