Treffer: High‐Accuracy Classification of Radiation Waveforms of Lightning Return Strokes.

A machine‐learning classifier for radiation waveforms of negative return strokes (RSs) is built and tested based on the Random Forest classifier using a large data set consisting of 14,898 negative RSs and 159,277 intracloud (IC) pulses with 3‐D location information. Eleven simple parameters including three parameters related with pulse characteristics and eight parameters related with the relative strength of pulses are defined to build the classifier. Two parameters for the evaluation of the classifier performance are also defined, including the classification accuracy, which is the percentage of true RSs in all classified RSs, and the identification efficiency, which is the percentage of correctly classified RSs in all true RSs. The tradeoff between the accuracy and the efficiency is examined and simple methods to tune the tradeoff are developed. The classifier achieved the best overall performance with an accuracy of 98.84% and an efficiency of 98.81%. With the same technique, the classifier for positive RSs is also built and tested using a data set consisting of 8,700 positive RSs. The classifier has an accuracy of 99.04% and an efficiency of 98.37%. By examining misclassified waveforms, we show evidence that some RSs and IC discharges produce special radiation waveforms that are almost impossible to correctly classify without 3‐D location information, resulting in a fundamental difficulty to achieve very high accuracy and efficiency in the classification of lightning radiation waveforms. Plain Language Summary: Lightning location systems are required to classify return strokes (RSs) from intracloud (IC) discharges accurately and efficiently because the RS is the main discharge component that poses direct threats to the human society. In this paper, we report a machine‐learning classifier for negative RSs built using a large data set with accurate 3‐D location information. The classifier has an accuracy of 98.84% (98.84% of classified RSs are correct classifications) and an efficiency of 98.81% (98.81% of RSs can be correctly classified). With the same technique, we also built a classifier for positive RSs with similarly high accuracy and efficiency. Our classifiers only require some simple waveform parameters so the same technique can be used by various lightning location systems relatively easily. A sample Python script to use the classifier is provided and readers are encouraged to test the classifier using their own data set. We also demonstrate that some RSs and IC discharges produce abnormal waveforms, so 100% accuracy or efficiency is fundamentally difficult to realize using only waveform information. Key Points: A machine‐learning classifier for negative return strokes (RSs) is built using a large data set with 3‐D location informationBoth an accuracy and an efficiency of about 98.8% are achieved and the accuracy‐efficiency tradeoff can be easily controlledSome RSs and intracloud discharges produce special waveforms that are fundamentally difficult to classify without 3‐D location results [ABSTRACT FROM AUTHOR]

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