Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-2943-2022
https://doi.org/10.5194/nhess-22-2943-2022
Research article
 | 
06 Sep 2022
Research article |  | 06 Sep 2022

A satellite lightning observation operator for storm-scale numerical weather prediction

Pauline Combarnous, Felix Erdmann, Olivier Caumont, Éric Defer, and Maud Martet

Related authors

Assimilation of surface pressure observations from personal weather stations in AROME-France
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024,https://doi.org/10.5194/nhess-24-907-2024, 2024
Short summary
Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France – proof of concept
Felix Erdmann, Olivier Caumont, and Eric Defer
Nat. Hazards Earth Syst. Sci., 23, 2821–2840, https://doi.org/10.5194/nhess-23-2821-2023,https://doi.org/10.5194/nhess-23-2821-2023, 2023
Short summary
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022,https://doi.org/10.5194/amt-15-5415-2022, 2022
Short summary
A numerical study to investigate the roles of former Hurricane Leslie, orography and evaporative cooling in the 2018 Aude heavy-precipitation event
Marc Mandement and Olivier Caumont
Weather Clim. Dynam., 2, 795–818, https://doi.org/10.5194/wcd-2-795-2021,https://doi.org/10.5194/wcd-2-795-2021, 2021
Short summary
W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021,https://doi.org/10.5194/amt-14-4929-2021, 2021
Short summary

Related subject area

Atmospheric, Meteorological and Climatological Hazards
Interannual variations in the seasonal cycle of extreme precipitation in Germany and the response to climate change
Madlen Peter, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 24, 1261–1285, https://doi.org/10.5194/nhess-24-1261-2024,https://doi.org/10.5194/nhess-24-1261-2024, 2024
Short summary
Climatology of large hail in Europe: characteristics of the European Severe Weather Database
Faye Hulton and David M. Schultz
Nat. Hazards Earth Syst. Sci., 24, 1079–1098, https://doi.org/10.5194/nhess-24-1079-2024,https://doi.org/10.5194/nhess-24-1079-2024, 2024
Short summary
Amplified potential for vegetation stress under climate-change-induced intensifying compound extreme events in the Greater Mediterranean Region
Patrick Olschewski, Mame Diarra Bousso Dieng, Hassane Moutahir, Brian Böker, Edwin Haas, Harald Kunstmann, and Patrick Laux
Nat. Hazards Earth Syst. Sci., 24, 1099–1134, https://doi.org/10.5194/nhess-24-1099-2024,https://doi.org/10.5194/nhess-24-1099-2024, 2024
Short summary
Assimilation of surface pressure observations from personal weather stations in AROME-France
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024,https://doi.org/10.5194/nhess-24-907-2024, 2024
Short summary
An open-source radar-based hail damage model for buildings and cars
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024,https://doi.org/10.5194/nhess-24-847-2024, 2024
Short summary

Cited articles

Allen, B. J., Mansell, E. R., Dowell, D. C., and Deierling, W.: Assimilation of pseudo-GLM data using the ensemble Kalman filter, Mon. Weather Rev., 144, 3465–3486, https://doi.org/10.1175/MWR-D-16-0117.1, 2016. a
Barthe, C. and Pinty, J.-P.: Simulation of a supercellular storm using a three-dimensional mesoscale model with an explicit lightning flash scheme, J. Geophys. Res., 112, D06210, https://doi.org/10.1029/2006JD007484, 2007. a
Bateman, M., Mach, D., and Stock, M.: Further Investigation Into Detection Efficiency and False Alarm Rate for the Geostationary Lightning Mappers Aboard GOES‐16 and GOES‐17, Earth Space Sci., 8, e2020EA001237, https://doi.org/10.1029/2020EA001237, 2021. a
Breiman, L.: Random Forest, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteorol. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Download
Short summary
The objective of this study is to prepare the assimilation of satellite lightning data in the French regional numerical weather prediction system. The assimilation of lightning data requires an observation operator, based on empirical relationships between the lightning observations and a set of proxies derived from the numerical weather prediction system variables. We fit machine learning regression models to our data to yield those relationships and to investigate the best proxy for lightning.
Altmetrics
Final-revised paper
Preprint