Articles | Volume 12, issue 10
https://doi.org/10.5194/amt-12-5573-2019
https://doi.org/10.5194/amt-12-5573-2019
Research article
 | 
22 Oct 2019
Research article |  | 22 Oct 2019

Analysis of the lightning production of convective cells

Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi

Related authors

Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations
Martin Lainer, Jordi Figueras i Ventura, Zaira Schauwecker, Marco Gabella, Montserrat F.-Bolaños, Reto Pauli, and Jacopo Grazioli
Atmos. Meas. Tech., 14, 3541–3560, https://doi.org/10.5194/amt-14-3541-2021,https://doi.org/10.5194/amt-14-3541-2021, 2021
Short summary
Polarimetric radar characteristics of lightning initiation and propagating channels
Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi
Atmos. Meas. Tech., 12, 2881–2911, https://doi.org/10.5194/amt-12-2881-2019,https://doi.org/10.5194/amt-12-2881-2019, 2019
Short summary
Unraveling hydrometeor mixtures in polarimetric radar measurements
Nikola Besic, Josué Gehring, Christophe Praz, Jordi Figueras i Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018,https://doi.org/10.5194/amt-11-4847-2018, 2018
Short summary
Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach
Nikola Besic, Jordi Figueras i Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016,https://doi.org/10.5194/amt-9-4425-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024,https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024,https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary
Noise filtering options for conically scanning Doppler lidar measurements with low pulse accumulation
Eileen Päschke and Carola Detring
Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024,https://doi.org/10.5194/amt-17-3187-2024, 2024
Short summary
Measuring rainfall using microwave links: the influence of temporal sampling
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 2811–2832, https://doi.org/10.5194/amt-17-2811-2024,https://doi.org/10.5194/amt-17-2811-2024, 2024
Short summary
Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024,https://doi.org/10.5194/amt-17-2539-2024, 2024
Short summary

Cited articles

Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a
Buiat, M., Porcù, F., and Dietrich, S.: Observing relationships between lightning and cloud profiles by means of a satellite-borne cloud radar, Atmos. Meas. Tech., 10, 221–230, https://doi.org/10.5194/amt-10-221-2017, 2017. a
Carey, L. D. and Rutledge, S. A.: The Relationship between Precipitation and Lightning in Tropical Island Convection: A C-Band Polarimetric Radar Study, Mon. Weather Rev., 128, 2687–2710, https://doi.org/10.1175/1520-0493(2000)128<2687:TRBPAL>2.0.CO;2, a
Doviak, R. and Zrnic, D.: Doppler Radar and Weather Observations, Dover Books on Engineering Series, Dover Publications, Mineola, New York, available at: https://books.google.ch/books?id=ispLkPX9n2UC (last access: 25 September 2019), 2006. a
Emersic, C., Heinselman, P. L., MacGorman, D. R., and Bruning, E. C.: Lightning Activity in a Hail-Producing Storm Observed with Phased-Array Radar, Mon. Weather Rev., 139, 1809–1825, https://doi.org/10.1175/2010MWR3574.1, a
Download
Short summary
This paper presents an analysis of the lightning production of convective cells. Polarimetric weather radar data were used to identify and characterize the convective cells while lightning was detected using the EUCLID network and a lightning mapping array deployed during the summer of 2017 in the northeastern part of Switzerland. In it we show that there is a good correlation between the height of the rimed-particle column and the intensity of the lightning activity in the convective cell.