Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability^☆

Abstract

Changes in the Atmospheric Electric Field Signal (AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means (FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree (MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

Keywords

Atmospheric electric field (AEF) / Thunderstorm / Fuzzy C-means (FCM) / Attribute

CRediT authorship contribution statement

Xu Yang: Writing - review & editing, Writing - original draft, Visualization, Validation, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software. Hongyan Xing: Project administration, Investigation, Funding acquisition, Data curation, Resources, Supervision. Xinyuan Ji: Data curation, Methodology, Software. Wei Xu: Data curation, Methodology, Resources, Software. Witold Pedrycz: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing - original draft, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 62171228, in part by the National Key R&D Program of China under Grant 2021YFE0105500, and in part by the Program of China Scholarship Council under Grant 202209040027.

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