An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

Abstract

Thunderstorm detection based on the Atmospheric Electric Field (AEF) has evolved from time-domain models to space-domain models. It is especially important to evaluate and determine the particularly Weather Attribute (WA), which is directly related to the detection reliability and authenticity. In this paper, a strategy is proposed to integrate three currently competitive WA's evaluation methods. First, a conventional evaluation method based on AEF statistical indicators is selected. Subsequent evaluation approaches include competing AEF-based predicted value intervals, and AEF classification based on fuzzy c-means. Different AEF attributes contribute to a more accurate AEF classification to different degrees. The resulting dynamic weighting applied to these attributes improves the classification accuracy. Each evaluation method is applied to evaluate the WA of a particular AEF, to obtain the corresponding evaluation score. The integration in the proposed strategy takes the form of a score accumulation. Different cumulative score levels correspond to different final WA results. Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes. Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms, with a 100% accuracy of WA evaluation. This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation, which provides promising solutions for a more reliable and flexible thunderstorm detection.