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Thunderstorms are a common natural phenomenon posing significant hazards to power systems, structures, and humans. With technological advancements, protection against lightning is gradually shifting from passive to active measures, which require the prediction of thunderstorm occurrences. Current research on lightning warning relies on various data sources, such as satellite data and atmospheric electric field data. However, these studies have placed greater emphasis on the process of warning implementation, overlooking the correlation between parameters used for lightning warning and lightning phenomena. This study relied on the ERA5 dataset and lightning location dataset from 117.5°E to 119.5°E longitude and 24.5°N to 25.5°N latitude during 2020–2021, utilizing Kriging interpolation to standardize the spatiotemporal precision of different parameters. After that, we conducted preliminary screening of the involved parameters based on the chi-squared test and utilized the Apriori algorithm to identify parameter intervals that were strongly associated with the occurrence of lightning. Subsequently, we extracted strong association rules oriented towards the occurrence of lightning and analyzed those rules with respect to lightning current amplitude, types, and ERA5 parameters. We found that thunderstorm phenomena are more likely to occur under specific ranges of temperature, humidity, and wind speed conditions, and we determined their parameter ranges. After that, we divided the target area into regions with different levels of lightning probability based on the strong association rules. By comparing the actual areas where lightning phenomena occurred with the areas at high risk of lightning based on ERA5 parameters, we validated the credibility of the obtained strong association rules.
Zhang et al. (Mon,) studied this question.