Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields.

热带气旋 (TC) 是最具破坏性的自然灾害之一，对社会，尤其是沿海地区的社会构成严重威胁。在这项工作中，我们利用气候网络研究了与 TC 发生相关的区域天气条件的时间演变。气候网络对地球表面不同位置的气候变量之间的相互作用进行编码，特别是随时间演变的气候网络已成功应用于研究相对较长时间尺度的不同气候现象，例如厄尔尼诺南方涛动、不同的季风系统，或火山爆发的气候影响。在这里，我们开发并应用了一种复杂的网络方法，适用于研究相对短命的 TC。我们表明，我们提出的方法有可能从平均海平面压力 (MSLP) 数据中识别 TC 及其轨迹。我们使用 ERA5 再分析 MSLP 数据为所考虑的区域构建连续的重叠、短时间窗口网络，我们关注北印度洋和热带北大西洋。我们比较了网络的各种拓扑特性的空间特征，以及在没有和存在气旋的情况下所涉及的空间尺度。我们发现诸如度数和聚类之类的网络度量表现出 TC 的重要特征，并且与它们的轨迹具有惊人的相似性。