We have developed a novel method for retrieving the single-point lower ionospheric density profile, utilizing observations from Loran-C and a ray-theory-based Earth-Ionosphere WaveGuide (RT-EIWG) model. Compared to other signals probing the D layer, such as VLF transmitters which provide path-averaged information on the upper boundary, and lightning sferics, which are less stable and dependent on the unpredictable occurrence of lightning, Loran-C offers significant advantages. The reflections of the Loran-C signal are clearly resolved over distances of 300-600 km. Additionally, the 1-hop reflection does not overlap with subsequent hops, and the signal is consistently emitted along a fixed path (interval ranges from 40 to 100 milliseconds). The RT-EIWG model effectively simulates ionospheric reflections at distances less than 1000 km. By combining the RT-EIWG model with Loran-C observations, we have established a new method for determining the electron density profiles of the D layer. Initial results indicate that the nighttime lower ionosphere varies with a period of a few minutes consistently. The method would be a powerful tool for the research of lower ionosphere interaction in the future.

Narrow Bipolar Events (NBEs) are powerful radio emissions from thunderstorms which have been recently associated with blue optical flashes on cloud tops and attributed to extensive streamer electrical discharges named fast breakdown. Combining data obtained from a thunderstorm over South China by the space-based Atmosphere Space Interactions Monitor (ASIM), the Vaisala GLD360 global lightning network and very low frequency (VLF)/low frequency (LF) radio detectors, here we report and analyze for the first time the optical emissions of Blue LUminous Events (BLUEs) associated with negative NBEs and located at the top edge of a thundercloud. These emissions are weakly affected by scattering from cloud droplets, allowing us to estimate the source extension and optical energy involved in the process. The optical energy in the 337-nm band emitted by fast breakdown is about 10^4 J, which involves around 10^9 streamer initiation events.

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.