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.

翻译：热带气旋是最具破坏性的天气系统之一。真实且高效地检测与追踪热带气旋对于评估其影响和风险至关重要。近期，一种多层级鲁棒性框架被引入用于研究时变向量场的临界点，该框架通过量化临界点在不同邻域尺度下的鲁棒性，并将其与临界点追踪关联，展现出在气旋追踪领域的潜力。其优势在于仅需二维风矢量场即可识别气旋特征——这具有重要意义，因为大多数追踪算法需依赖不同高度层的多个动力学与热力学变量。其不足在于，当数据集中包含大量气旋时，该框架的计算可扩展性较差。本文提出一种面向热带气旋追踪的拓扑鲁棒物理信息追踪框架(TROPHY)，核心思想是通过融合热带气旋物理知识，大幅提升多层级鲁棒性框架在大规模气候数据集上的计算效率。首先，在预处理阶段，我们提出物理信息驱动的特征选择策略，可过滤90%短时存活且稳定性低的临界点，从而保留适用于热带气旋追踪的候选点。其次，在处理过程中，我们在多层级鲁棒性计算中施加约束，仅关注热带气旋的物理信息邻域。我们将TROPHY应用于ERA5再分析数据中30年的二维风场，生成了大量热带气旋轨迹。与观测轨迹对比表明，TROPHY能够捕捉与经充分验证（需多个动力学与热力学标量场）的热带气旋追踪算法相当甚至更优的气旋特征。