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 topologically robust
physics-informed tracking framework (TROPHY) for TC tracking. The main idea is
to integrate physical knowledge of TC to drastically improve the computational
efficiency of multilevel robustness framework for large-scale climate datasets.
First, during preprocessing, we propose a physics-informed feature selection
strategy to filter 90% of critical points that are short-lived and have low
stability, thus preserving good candidates for TC tracking. Second, during
in-processing, we impose constraints during the multilevel robustness
computation to focus only on physics-informed neighborhoods of TCs. We apply
TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate
a number of TC tracks. In comparison with the observed tracks, we demonstrate
that TROPHY can capture TC characteristics that are comparable to and sometimes
even better than a well-validated TC tracking algorithm that requires multiple
dynamic and thermodynamic scalar fields.

该研究提出了一种基于物理先验知识的强拓扑物理追踪框架（TROPHY），用于大规模气候数据集中的热带气旋追踪，通过引入限制条件和物理知识选择方法，实现了低计算复杂度同时获得与传统追踪算法相媲美甚至更好的热带气旋特征捕捉。