Investigation on the Lightning Location and Warning System Using Artificial Intelligence

An in-depth study on a lighting location system is conducted in this paper. Firstly, the history and application of this system are summarized. The overall structure is detailed, including the detection principle of the lightning location, the orientation method, the detection circuit, the method of discriminating cloud flash and ground lightning signal, the error analysis, the guideline for station deployment, the preprocessing of the central station, and the function and structure of data server and user interface. The development of a lightning monitoring system in China is presented, and the construction of a new generation of a lightning location system in the Hubei Province power grid is introduced. Through the collection of measured data, the performance of the lightning location system in the lightning accident inspection rate, lightning location, and lightning situation statistics are analyzed. Artificial intelligence algorithms are applied in the lightning warning system. The new system has a high predicting accuracy.

Synoptic patterns favouring lightning-ignited wildfires in Catalonia

<p>Wildfires cause substantial losses to socio-economic and natural assets, especially in Mediterranean-climate regions. Despite human activity is the main cause of wildfires in Mediterranean European countries, lightning-ignited wildfires should be also considered a major disruptive agent as they can trigger large fires. Besides, recent studies on the potential climate change effects on wildfires pointed out that lightning-ignited wildfires may gain relevance in Mediterranean areas in the years to come.</p><p>In this regard, the present study analyses the meteorological conditions favouring lightning-ignited wildfires in Catalonia (NE Iberian Peninsula). Gaining insight into circulation types favouring thunderstorms that ignite wildfires can be useful in the forest protection tactical decision-making process, i.e. locating ignitions and potential holdover fires, preparing for days with multiple ignitions or routing detection flight paths.</p><p>It is worth noticing that one of the reasons why lightning-caused wildfires are difficult to manage is that they can survive for several days before flaring up. That is, even if forest fuels remain damp after the thunderstorm’ rainfall, lightning ignitions may survive smouldering underneath, emerging days later as surface vegetation becomes dry enough to support sustained combustion.</p><p>For this reason, on a first step, a reliable lightning-wildfire association is needed to properly identify the date and time of the firestarter for each wildfire. Afterwards, the circulation types on the days of ignition are analysed.</p><p>The study relies on a dataset of more than 750 lightning-ignited wildfires, gathered by the Forest Protection Agency of the autonomous government of Catalonia between 2005 and 2018. Lightning data comes from the Lightning Location System operated by the Meteorological Service of Catalonia.</p>

Effects of Day/Night Factor on the Detection Performance of FY4A Lightning Mapping Imager in Hainan, China

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DELMI) were 20.41~35.53% lower than the nighttime DELMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DELMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DELMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr−1 m−2 nm−1). However, during daytime, signals with RED less than 77.12 μJ sr−1 m−2 nm−1 were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.

Spatiotemporal lightning activity detected by WWLLN over Tibetan Plateau and its comparison with LIS lightning

Herein, we compared data on the spatiotemporal distribution of lightning activity obtained from the World Wide Lightning Location Network (WWLLN) with that from the Lightning Imaging Sensor (LIS). The WWLLN and LIS both suggest intense lightning activity over the central and southeastern Tibetan Plateau (TP) during May–September. Meanwhile, the WWLLN indicates relatively weak lightning activity over the northeastern TP, where the LIS suggests very intense lightning activity, and it also indicates a high-density lightning center over the southwestern TP, not suggested by the LIS. Furthermore, the WWLLN lightning peaks in August in terms of monthly variation and in late August in terms of ten-day variation, unlike the corresponding LIS lightning peaks of July and late June, respectively. Other observation data were also introduced into the comparison. The black body temperature (TBB) data from the Fengyun-2E geostationary satellite (as a proxy of deep convection) and thunderstorm day data support the spatial distribution of the WWLLN lightning more. Meanwhile, for seasonal variation, the TBB data is more analogous to the LIS data, while the cloud-to-ground (CG) lightning data from a local CG lightning location system is closer to the WWLLN data. It is speculated that the different WWLLN and LIS observation modes may cause their data to represent different dominant types of lightning, thereby leading to differences in the spatiotemporal distributions of their data. The results may further imply that there exist regional differences and seasonal variations in the electrical properties of thunderstorms over the TP.

Asia-Pacific Lightning Location Network (APLLN) and Preliminary Performance Assessment

The Asia-Pacific Lightning Location Network (APLLN) is a lightning location system consisting of a series of very low-frequency signal detection sites. Since 2018, 16 detection sites have been deployed with an average baseline longer than 1000 km. The detection site used a trigger sampling method to record the lightning signal with a duration of 2 ms and calculates the lightning arrival time based on digital filtering and the Hilbert envelope method. APLLN used a time difference location algorithm and improved Levenberg–Marquardt non-linear least squares iterative algorithm to calculate and optimize the lightning location results. The analysis results of a strong thunderstorm process show that the average detection efficiency of APLLN was 55.34% for intracloud (IC) strokes, 63.55% for cloud-to-ground (CG) strokes and 61.83% for all strokes (IC + CG). The average location error of APLLN for this thunderstorm is 5–10 km.