Characterization of thunderstorms to detect, track and extrapolate supercells.

Supercells represent a major safety issue due to violent phenomena they can provoke, such as large hail, severe wind gust, heavy rain and, sometimes, tornadoes.That’s why this issue has been taken head-on and a dedicated product for purposes of detection, tracking and nowcasting of supercells was developed.

For this implementation and evaluation of the algorithm, an extensive study of severe thunderstorms and, in particular, supercells, has been performed. A national database of severe convection events (multicell, supercell, isolated cell), their location and evolution in time, is first created from the ESWD database.

This national database is then further improved by two existing products at Météo France:

(i)- Detection of mesocyclones using the French radar network. Radial velocity field from Doppler radars is used as criteria to identify the presence of mesocyclones. This technique is based on work by Zrnic et al. (1985), largely inspired by Hengstebeck et al. (2018) and presented at ERAD 2022 by Tony Le Bastard.

(ii)- Object for convection nowcasting (a.k.a. OPIC), developed by Bernard-Bouissieres I. at DIROP/PI (*). It is an operational product based on reflectivity field and the satellite observations of lightnings. It provides various information about geometry, reflectivity, direction and speed of movement, lightning and other…

As a result, the upgrade version of the database includes the spatial properties of storm cells: altitude, geometry, reflectivity and precipitation, direction and speed movement, lightning, mesocyclones, shear as well as the evolution of these parameters during the lifetime of thunderstorms.

The OPICs are used for the detection, tracking and nowcasting of convective cells. Mesocyclone detection and the application of certain criteria extracted from a database study make it possible to identify supercells among other cell types.

The preliminary results as well as the product limitation and advantages will be presented.

 

*. dirop_pi@meteo.fr

Zrnić, D. S., Burgess, D. W., & Hennington, L. D. (1985). Automatic detection of mesocyclonic shear with Doppler radar. Journal of Atmospheric and Oceanic Technology, 2(4), 425-438, https://doi.org/ 10.1175/1520-0426(1985)002,0425:ADOMSW.2.0.CO;2

Hengstebeck, T., Wapler, K., Heizenreder, D., & Joe, P. (2018). Radar Network–Based Detection of Mesocyclones at the German Weather Service. Journal of Atmospheric and Oceanic Technology, 35(2), 299-321,https://doi.org/10.1175/JTECH-D-16-0230.1

Imbert, J. (2019). Conception et évaluation d’un algorithme de détection automatique des mésocyclone. Rapport de stage de fin d'étude IENM.

Le Bastard T. (2022). Mesocyclone Detection at Météo-France, presented at ERAD 2022.