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Author: aurelien.houard

The Polarity Reversal of Lightning- Generated Sky Wave

The Polarity Reversal of Lightning- Generated Sky Wave

W. Hou; M. Azadifar; M. Rubinstein; F. Rachidi; Q. Zhang, “The Polarity Reversal of Lightning- Generated Sky Wave,” Journal of Geophysical Research: Atmospheres. 2020. Vol. 125, num. 17, p. 1-17, e2020JD032448. https://doi.org/10.1029/2020JD032448

The polarity reversal of the lightning-generated first sky wave as a function of the observation distance is studied using a novel approach combining the finite-difference time domain (FDTD) method and the superposition principle of electromagnetic waves. In this method, the sky wave is generated by radiation from the induced current produced by the motion of charged particles driven by the lightning-radiated electromagnetic waves in the ionosphere. The horizontal and vertical components of the induced current density under the daytime and nighttime ionospheric conditions are evaluated. Their different contributions to the sky wave at different observation distances are analyzed in detail. Furthermore, a physical explanation for the polarity reversal in the time domain is proposed. It is found that, for relatively short observation distances (within ~200 km), the first sky wave is dominated by the component generated by the horizontal equivalent current in the Fresnel zone, while for longer observation distances (larger than ~300 km), the first sky wave is dominated by the component generated by the vertical equivalent current in the Fresnel zone. Since the polarities of the sky wave components generated by the vertical current source and horizontal current source are opposite, the polarity of the sky wave will reverse when increasing the observation distance.

Molecular quantum wakes for clearing fog

Molecular quantum wakes for clearing fog

M. C. Schroeder, I. Larkin, T. Produit, E. W. Rosenthal, H. Milchberg and J.-P. Wolf, “Molecular quantum wakes for clearing fog”, Optics Express 28, 11463 (2020) https://doi.org/10.1364/OE.389393

High intensity laser filamentation in air has recently demonstrated that, through plasma generation and its associated shockwave, fog can be cleared around the beam, leaving an optically transparent path to transmit light. However, for practical applications like free-space optical communication (FSO), channels of multi-centimeter diameters over kilometer ranges are required, which is extremely challenging for a plasma based method. Here we report a radically different approach, based on quantum control. We demonstrate that fog clearing can also be achieved by producing molecular quantum wakes in air, and that neither plasma generation nor filamentation are required. The effect is clearly associated with the rephasing time of the rotational wave packet in N2.Pump excitation provided in the form of resonant trains of 8 pulses separated by the revival time are able to transmit optical data through fog with initial extinction as much as −6 dB.

Numerical and Experimental Validation of Electromagnetic Time Reversal for Geolocation of Lightning Strikes

Numerical and Experimental Validation of Electromagnetic Time Reversal for Geolocation of Lightning Strikes

H. Karami; M. Azadifar; A. Mostajabi; M. Rubinstein; F. Rachidi, “Numerical and Experimental Validation of Electromagnetic Time Reversal for Geolocation of Lightning Strikes,” IEEE Transactions on Electromagnetic Compatibility. 2020. Vol. 62, num. 5, p. 2156-2163. https://doi.org/10.1109/TEMC.2019.2957531

We implement an electromagnetic time reversal technique (EMTR) to locate lightning return strokes. The two-dimensional finite difference time domain is employed to simulate the EMTR process in both, the forward-time and the backward-time phases. Scatterers are included in the computational domain to emulate the presence of objects. Three possible criteria to find the optimum time slice of the EMTR process that includes the maximum peak field, maximum peak energy, and last local minimum of entropy are tested and it is found that only the entropy criterion can successfully locate the lightning discharge. Our analysis shows that the EMTR process in both, using an unchanged and a simplified medium for the backward time works reasonably well even with only two sensors. Furthermore, we validated the proposed method via experimental results using waveforms recorded at two sensors at distances of 14.7 and 380 km from the Säntis Tower. The results demonstrate that the EMTR back-propagation process leads to a refocusing of the radiated energy at the location of the Säntis Tower. The ambiguity in the obtained location when only two sensors are used can be resolved either by using an additional sensor or through a more accurate modeling of the terrain.

Article on lightning prediction using AI

Article on lightning prediction using AI

L'intelligenza artificiale prevede l'arrivo dei fulmini (fonte: Pixabay) © Ansa

Italian journal ANSA wrote an article on the recent results of EPFL and HES-SO concerning the prediction of lighting using Artificial Intelligence: link

A. Mostajabi et al. “Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning,” Scientific Reports. 2019-11-22. Vol. 9, num. 1. https//doi.org/10.1038/s41598-019-53934-4

LMA observations of upward lightning flashes at the Säntis Tower initiated by nearby lightning activity

LMA observations of upward lightning flashes at the Säntis Tower initiated by nearby lightning activity

A. Sunjerga; M. Rubinstein; N. Pineda; A. Mostajabi; M. Azadifar et al. “LMA observations of upward lightning flashes at the Säntis Tower initiated by nearby lightning activity,” Electr. Power Syst. Res., p. 106067, Dec. 2019, doi: 10.1016/j.epsr.2019.106067

Abstract: We present in this paper lightning current measurements, LMA (Lightning Mapping Array) data and fast antenna electric fields associated with upward flashes observed at the Säntis Tower during summer of 2017. The LMA network consists of six stations that were installed in the vicinity of the tower at distances ranging from 100 m to 11 km from it. Out of 20 LMA recorded flashes here we analyze in detail three so-called ‘other-triggered flashes’, triggered by preceding activity. Based on the lightning activity derived from the European Lightning Detection Network (EUCLID) in an area within 30 km from the tower and within a 1-s time window before the start of the upward tower flashes, only one out of 20 flashes was classified as ‘other-triggered’(OT). However, the investigations based on the LMA data reveal that 3 more flashes of the 20 analyzed were preceded by nearby activity and should therefore be classified as OT flashes. We analyze conditions conducive to the OT flashes, such as the charge structure of the clouds, polarity of preceding leaders and level of activity of the storm. The LMA source active time period was on average seven times higher for the OT flashes than that for self-initiated flashes.

A New Engineering Model of Lightning M Component That Reproduces Its Electric Field Waveforms at Both Close and Far Distances

A New Engineering Model of Lightning M Component That Reproduces Its Electric Field Waveforms at Both Close and Far Distances

M. Azadifar, M. Rubinstein, Q. Li, F. Rachidi, and V. Rakov, “A New Engineering Model of Lightning M Component That Reproduces Its Electric Field Waveforms at Both Close and Far Distances,” J. Geophys. Res. Atmospheres, vol. 124, no. 24, pp. 14008–14023, 2019, https://doi.org/10.1029/2019JD030796

Abstract: We present a new engineering model for the M component mode of charge transfer to ground that can predict the observed electric field signatures associated with this process at various distances, including (a) the microsecond-scale pulse thought to be due to the junction of in-cloud leaders and the grounded, current-carrying channel and (b) the ensuing slow, millisecond-scale pulse due to the M component proper occurring below the junction point.

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning

A. Mostajabi; H. Karami; M. Azadifar; A. Ghasemi; M. Rubinstein et al. “Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning,” Scientific Reports. 2019-11-22. Vol. 9, num. 1. https://doi.org/10.1038/s41598-019-53934-4

Electromagnetic Time Reversal (EMTR) has been used to locate different types of electromagnetic sources. We propose a novel technique based on the combination of EMTR and Machine Learning (ML) for source localization. We show for the first time that ML techniques can be used in conjunction with EMTR to reduce the required number of sensors to only one for the localization of electromagnetic sources in the presence of scatterers. In the EMTR part, we use 2D-FDTD method to generate 2D profiles of the vertical electric field as RGB images. Next, in the ML part, we take advantage of transfer learning techniques by using the pretrained VGG-19 Convolutional Neural Network (CNN) as the feature extractor tool. To the best of our knowledge, this is the first time that the knowledge of pretrained CNNs is applied to simulation-generated images. We demonstrate the skill of the developed methodology in localizing two kinds of electromagnetic sources, namely RF sources with a bandwidth of 0.1–10 MHz and lightning impulses. For the localization of lightning, based on the experimental recordings in the Säntis region, the new approach enables accurate 2D lightning localization using only one sensor, as opposed to current lightning location systems that need at least two sensors to operate.

Analysis of the lightning production of convective cells

Analysis of the lightning production of convective cells

J. Figueras i Ventura, N. Pineda, N. Besic, J. Grazioli, A. Hering, O. A. van der Velde, D. Romero, A. Sunjerga, A. Mostajabi, M. Azadifar, M. Rubinstein, J. Montanyà, U. Germann, and F. Rachidi, “Analysis of the lightning production of convective cells,” Atmospheric Meas. Tech., vol. 12, no. 10, pp. 5573–5591, Oct. 2019

https://doi.org/10.5194/amt-12-5573-2019

Abstract: This paper presents an analysis of the lightning production of convective cells. The cells were detected by the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm in the course of a lightning measurement campaign that took place in the summer of 2017 in the area surrounding the Säntis mountain, in the northeastern part of Switzerland. For this campaign, and for the first time in the Alps, a lightning mapping array (LMA) was deployed. In the first part of the paper, we examine the relationship between the intra-cloud (IC) and cloud-to-ground (CG) activity and the cell severity, as derived by the TRT algorithm, of a large dataset of cells gathered during the campaign. We also propose and analyse the performance of a new metric to quantify lightning intensity, the rimed-particle column (RPC) height and base altitude. In the second part, we focus on two of the most severe cells detected during the campaign that produced significantly different outcomes in terms of lightning activity. The paper shows that the newly proposed metric (RPC) seems to be a very promising predictor of lightning activity, particularly for IC flashes. Future lightning nowcasting algorithms should be probabilistic in nature and incorporate the polarimetric properties of the convective cells as well as the lightning climatology.