Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.

Severe weather events are responsible for hundreds of fatalities and millions of euros of damage every year on the Mediterranean basin. Lightning activity is a characteristic phenomenon of severe weather and often accompanies torrential rainfall, which, under certain conditions like terrain type, slope, drainage, and soil saturation, may turn into flash flood. Building on the existing relationship between significant lightning activity and deep convection and precipitation, the performance of the Lightning Potential Index, as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, is here evaluated for the V‐shape back‐building Mesoscale Convective System which hit Genoa city (Italy) in 2014. An ensemble of Weather Research and Forecasting simulations at cloud‐permitting grid spacing (1 km) with different microphysical parameterizations is performed and compared to the available observational radar and lightning data. The results allow gaining a deeper understanding of the role of lightning phenomena in the predictability of V‐shape back‐building Mesoscale Convective Systems often producing flash flood over western Mediterranean complex topography areas. Moreover, they support the relevance of accurate lightning forecasting for the predictive ability of these severe events.

Electromagnetic time reversal (EMTR) has emerged as a promising technique to locate disturbances in power grids, thanks to its location accuracy and robustness against parameters uncertainties. Furthermore, in a reflective medium, like the one of a power network, it has been shown that the method requires no more than one single observation point. In this paper, we present an experimental validation of EMTR to locate disturbances in real power networks. The validation is performed on a full-scale unenergized 677-m-long, double-circuit 10-kV overhead distribution line. The disturbance is emulated by a voltage pulse injected between one of the line conductors and the ground using a high-voltage pulse generator. The frequency spectrum of the injected voltage pulse is specified such that the originated electromagnetic transients are compatible with those of power line faults, lightning, and conducted intentional electromagnetic interferences. The transient currents generated by the emulated disturbance are measured at one end of the line, considering two different line configurations. According to the EMTR technique, the measured signals are time reversed and back injected into the system that, in our case, is a simulated model of the considered distribution line. More specifically, it is represented by a constant-parameter line model implemented within the EMTP-RV simulation environment. For both cases, the disturbance is accurately located, and the phase of the circuit at which the pulse was injected is also identified.

In this work, we present a novel technique to locate partial discharge (PD) sources based on the concept of time reversal. The localization of the PD sources is of interest for numerous applications, including the monitoring of power transformers, Gas Insulated Substations, electric motors, super capacitors, or any other device or system that can suffer from PDs. To the best of the authors’ knowledge, this is the first time that the concept of time reversal is applied to localize PD sources. Partial discharges emit both electromagnetic and acoustic waves. The proposed method can be used to localize PD sources using either electromagnetic or acoustic waves. As a proof of concept, we present only the results for the electromagnetic case. The proposed method consists of three general steps: (1) recording of the waves from the PD source(s) via proper sensor(s), (2) the time-reversal and back-propagation of the recorded signal(s) into the medium using numerical simulations, and (3) the localization of focal spots. We demonstrate that, unlike the conventional techniques based on the time difference of arrival, the proposed time reversal method can accurately localize PD sources using only one sensor. As a result, the proposed method is much more cost effective compared to existing techniques. The performance of the proposed method is tested considering practical scenarios in which none of the former developed methods can provide reasonable results. Moreover, the proposed method has the unique advantage of being able to locate multiple simultaneous PD sources and doing so with a single sensor. The efficiency of the method against the variation in the polarization of the PDs, their length, and against environmental noise is also investigated. Finally, the validity of the proposed procedure is tested against experimental observations.

In this paper, we present a performance analysis of the European Cooperation for Lightning Detection (EUCLID) lightning detection network using data obtained on lightning currents measured at the Säntis Tower (located in northeastern of Switzerland) from June 2010 to December 2013. In the considered period of analysis, a total number of 269 upward negative flashes were recorded at the Säntis Tower. The performance of the EUCLID lightning detection network is evaluated in terms of detection efficiency, location accuracy, and peak current estimates for upward flashes. Excluding flashes containing only an initial continuous current with no superimposed pulses exceeding 2 kA, the flash detection efficiency for upward flashes is estimated to be 97%. The recorded flashes contained a total of 2795 pulses (including return strokes and International Conference on Communications pulses characterized by risetimes lower than 8 µs and peaks greater than 2 kA). The overall pulse detection efficiency was found to be 73%. For pulses with peak values higher than 5 kA, the pulse detection efficiency was found to be about 83%. Peak current estimates provided by the EUCLID network were found to be significantly larger than their directly measured counterparts. This overestimation might be attributed to the enhancement of the radiated electromagnetic fields associated with the presence of the tower and the mountain. The median of the absolute distance error, defined as the median distance between the Säntis Tower location and the EUCLID's stroke locations, was found to be 186 m, the majority of large location errors being associated with measured current peaks lower than 10 kA. The analysis revealed also that the location accuracy of the EUCLID network improved significantly in 2013 as a result of an upgrade in the location algorithms to take into account propagation effects.