Dynamic thunderstorm capture and path imaging driven by information granulation and fuzzy C-means clustering
Knowledge-Based Systems, vol.336, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 336
- Publication Date: 2026
- Doi Number: 10.1016/j.knosys.2026.115312
- Journal Name: Knowledge-Based Systems
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Library, Information Science & Technology Abstracts (LISTA)
- Keywords: Atmospheric electric field (AEF), Fuzzy C-means (FCM), Information granules, Knowledge, Thunderstorm
- Istanbul University Affiliated: No
Abstract
The mining of ambiguous weather attributes carried by atmospheric electric fields (AEFs), especially, the accurate estimation of thunderstorm occurrence times, is significantly challenging. In this paper, a method to capture thunderstorm time windows is proposed for the first time, based on fuzzy C-means (FCM) clustering. Information granules, i.e., fuzzy sets characterizing thunderstorms as dominant weather, are constructed by processing the time series AEF signal (AEFS) with first-order difference, statistics, and time attributes as data to be clustered by the FCM algorithm. Combined with labeled AEF cases, the optimal information granularity among multiple granularities is determined. Then, α-cut is applied to target granules to capture the temporal start and end points of granule elements representing thunderstorm dominance obtained by this cut. These time points correspond to a possible thunderstorm time window and the corresponding AEFS component. A first-step evaluation of the window's rationality is performed based on expert knowledge and weather variation rules. Only components passing the first-step evaluation and subsequently evaluated as thunderstorm-dominated by the designed expert experience fusion-based method are finally evaluated to be utilized for imaging thunderstorm cloud point charge moving paths in the spatial domain. Results show that the proposed method accurately captures the time window and reliably images thunderstorm moving paths in multiple cases. This interpretable dynamic-capture approach has significant advantages over those equal-time AEFS-division-based weather evaluations, facilitating real-time and high-efficiency thunderstorm monitoring.