Thunderstorm kernel identification and tracing method based on hybrid clustering algorithm

Abstract

The invention discloses a thunderstorm kernel identification and tracing method based on a hybrid clustering algorithm. The method specifically includes the following steps of utilizing deployed thunder and lightning monitoring points to conduct exploration and record cloud-to-ground lightning data, conducting preprocessing on the recorded cloud-to-ground lightning data and dividing the data intolightning data sets of each equal time interval; adopting a GPS clock synchronization technology and an algorithm of time differences of arrival for figuring out spatial positioning coordinates of lightning according to the time differences of arrival of changeable radiation pulses of an electric field generated by the lightning to each station; on the basis of thunder and lightning positioning data figured out by means of the procedures above, utilizing a DBSCAN algorithm and a KMEANS algorithm to figure out the relevance of the thunder and lightning positioning data among a thunderstorm kernel center-of-mass coordinate position, the lightning frequency and a thunderstorm kernel. An experimental result indicates that the method can accurately reflect the change tendency of thunder and lightning on thunderstorm days, and great effects of identifying the thunderstorm kernel and movably tracing a thunderstorm are achieved.

Description

technical field [0001] The invention belongs to the field of lightning monitoring and relates to a thunderstorm nucleus identification and tracking method based on a hybrid clustering algorithm. Background technique [0002] With the development of electronic technology and computer technology, thunderstorm lightning activity monitoring has developed from traditional lightning location observation to complete recording of the detailed characteristics of lightning activity during the entire thunderstorm life history. On this basis, various lightning data based on the evolution of thunderstorm life history can be developed product. Lightning activity is an indicator of the strength of thunderstorm convective activity. Compared with meteorological radar detection of thunderstorm cloud precipitation particles, the potential of diagnosing the timeliness and accuracy of strong convective activity has attracted more and more attention. Monitoring of thunderstorm convective activit...

AI Technical Summary

Problems solved by technology

However, when applied to the clustering of lightning data, the result is a cluster, not a "center", and the existing noise points cannot be distinguished

The key to the KMEANS algorithm is the selection of the K value. If the distribution of lightning location data is too scattered and aggregated according to a fixed K value, the position of the centroid obtained may be far from the actual position.

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