Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

A. Mostajabi, D. L. Finney, M. Rubinstein, F. Rachidi, npj Climate and Atmospheric Science 2, 41 (2019)

Abstract: 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.

Jean-Pierre WOLF (UNIGE) participation in the CQFD program of RTS, a Swiss Radio TV

A few words to thank JP. Wolf for his participation in the CQFD program on the topic: “Un paratonnerre laser pour diriger la foudre” on March 17, 2020.

To listen back and download this subject broadcasted on RTS, clic on the link below:

To listen to the entire program broadcasted on that day clic here.

Euronews video about LLR project

Euronews journalists met with the LLR team to prepare a video about the project under the FUTURIS program, “the latest news about the leading scientific and technological research projects in Europe”

The video article has been published on September 16th, 2019 on the Euronews website.

HV discharges triggered by dual- and triple-frequency laser filaments

T. Produit, P. Walch, G; Schimmel, B. Mahieu, C. Herkommer, R. Jung, T. Metzger, K. Michel, Y.-B. André, A. Mysyrowicz, A. Houard, J. Kasparian, J.-P. Wolf, Optics Express, 11339 (2019) link

Abstract: We study the use of frequency upconversion schemes of near-IR picosecond laserpulses and compare their ability to guide and trigger electric discharges through filamentationin air. Upconversion, such as Second Harmonic Generation, is favorable for triggering electricdischarges for given amount of available laser energy, even taking into account the losses inherentto frequency conversion. We focus on the practical question of optimizing the use of energy froma given available laser system and the potential advantage to use frequency conversion schemes.