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

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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
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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.
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