Lower ionosphere is difficult to probe for conventional measurements, as its altitude range is too low for the satellite and too high for atmospheric balloons. Recently, an alternative method to study lower ionosphere is using lightning signatures. In this work，we investigate the daytime fluctuations of lower ionosphere by analyzing the time domain VLF/LF waveform emitted by Narrow Bipolar Events (NBEs), which were recorded by an eight-station lightning location network called Jianghuai-Area Sferic Array (JASA) for a systemic real-time monitoring of lower ionosphere. JASA has been developed in 2012 and improved as a real-time lightning location network since 2018. We analyze numerous thunderstorm-day data and the preliminary results show that the lower ionosphere virtual height descends linearly at a rate of 5.9 km/h during sunrise, whereas rises linearly at a rate of 8.6 km/h during sunset. Moreover, the lower ionosphere virtual height short-time fluctuation is significantly modulated by the solar radiations’ flux density recorded by GOES satellite and the lower ionosphere diurnal variation pattern exhibiting a significant asymmetry, indicating that electron production in the lower ionosphere is dominated by the effect of solar radiation accelerating the neutral atmospheric molecules

With the application of lightning data in meteorology, electric power as well as public safety, massive lightning data is accumulated. Meanwhile, a synthesized and complicated problem arose, which is, how to automatically obtain the valuable information from the massive lightning data. The deep learning method provides an effective way to automatically classify the event type of lightning discharges from raw lightning data. In this paper, we propose a five-category classification model for the raw lightning waveforms in VLF/LF bands. The model is based on deep convolutional neural network, which is trained and tested by a six year (2012-2017) data set that comprised of over 30000 lightning events. We did experiment with different layers of networks and found that the 7-layer network gives the best performance. The output of the classifier is a five-element vector which shows up the results of different lightning type. Due to the multi-layer stacking of the convolutional network, higher-order features can be better extracted. Furthermore, the model we proposed can effectively identify the cloud-to-ground (CG) flash, ordinary intracloud (IC) flash, preliminary breakdown pulse (PB), narrow bipolar event (NBE), and especially the error rate on CG is less than 3%. Finally, we apply the classifier to the lightning data set for 2017 and group identified return stroke into flashes by hand to qualify the accuracy-stroke-study data of CG flashes. Based on the flashes, we present the characteristics of cloud-to-ground lightning flashes in four isolated small thunderstorms.