| The knowledge of lightning activities is important in lightning protection work and also in the fields of lightning forecast. But the spatial and temporal distributions of lightning activities are difficult to be measured for its randomicity and nonlinearity. Supported by the Natural Science Foundation of Jiangsu Province (KLME050101) in 2005, the Cloud to Ground lightning flash (CG) data from Lightning Location System (LLS) in Jiangsu Province has been analyzed to find out the rule of regional CG flashes. And a neural network based on the LLS data has been developed for lightning forecast.Since August in 2005, the Lightning Location System in Jiangsu province has been functioning for more than one year. The lightning data in the last year partly reveals the lightning activities in and around Jiangsu province, although the detection efficiency remains to be improved. The present study statistically analyses the data and find: (1) the most cloud to ground lightning fleshes(CG) occur during April to July, and the peak value of lightning activity was find to be around 15:00(LMT)(2) The spectra distribution of return stroke current intensions exhibits normal distribution (3) the prominent maxima of lightning density was find to be around Hongze lake and Yisu mountain area, which reflects that thunderstorm activities were well related to the surface characteristics. (4) the lightning density was not equable in different seasons.A neural network-based scheme to do a multivariate analysis of the occurrence of thunderstorm is presented. Many sounding-derived indices are combined together to build a short-term forecast of thunderstorm in the city of Nanjing. For thunderstorm forecasting , sounding and lightning strike data from June to September have been used to train and validate the network. Output form network show relatively satisfied performance in this preliminary study.
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