Articles | Volume 22, issue 9
Nat. Hazards Earth Syst. Sci., 22, 2943–2962, 2022
https://doi.org/10.5194/nhess-22-2943-2022
Nat. Hazards Earth Syst. Sci., 22, 2943–2962, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-39', Colin Price, 02 Apr 2022
    • AC1: 'Reply on RC1', Pauline Combarnous, 16 Jun 2022
  • RC2: 'Comment on nhess-2022-39', Eric Bruning, 05 Apr 2022
    • AC2: 'Reply on RC2', Pauline Combarnous, 16 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (19 Jun 2022) by Maria-Carmen Llasat
AR by Pauline Combarnous on behalf of the Authors (20 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (23 Jul 2022) by Maria-Carmen Llasat
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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.
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