Preprints
https://doi.org/10.5194/nhess-2023-103
https://doi.org/10.5194/nhess-2023-103
08 Aug 2023
 | 08 Aug 2023
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Assimilation of surface pressure observations from personal weather stations in AROME-France

Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont

Abstract. Spatially dense surface pressure observations from personal weather stations (PWSs) have proved their ability to describe physical meteorological patterns, such as convective events, which are not visible with the only use of standard weather stations (SWSs). In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the south of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWSs observations. Despite the small advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by −10.3 % the root-mean-square departure in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed up to 9 h of forecast in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system – observed by PWSs and not visible without them – is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated.

Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-103', Anonymous Referee #1, 06 Sep 2023
    • AC1: 'Reply on RC1', Alan Demortier, 30 Nov 2023
  • RC2: 'Comment on nhess-2023-103', Anonymous Referee #2, 27 Oct 2023
    • AC2: 'Reply on RC2', Alan Demortier, 30 Nov 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-103', Anonymous Referee #1, 06 Sep 2023
    • AC1: 'Reply on RC1', Alan Demortier, 30 Nov 2023
  • RC2: 'Comment on nhess-2023-103', Anonymous Referee #2, 27 Oct 2023
    • AC2: 'Reply on RC2', Alan Demortier, 30 Nov 2023
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont

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Short summary
Improvements in numerical weather prediction models make it possible to warn of hazardous weather situations. The incorporation of new observations from personal weather stations into the French limited-area model is evaluated. It leads to a significant improvement in the modelling of the surface pressure field up to 9 hours ahead. Their incorporation improves the location and intensity of the heavy precipitation event that occurred in the south of France in September 2021.
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