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Discussion papers
https://doi.org/10.5194/nhess-2019-302
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-302
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 25 Sep 2019

Submitted as: research article | 25 Sep 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Systematic errors analysis of heavy precipitating events prediction using a 30-year hindcast dataset

Matteo Ponzano, Bruno Joly, Laurent Descamps, and Philippe Arbogast Matteo Ponzano et al.
  • CRNM/GAME, Météo-France/CRNS URA 1357, Toulouse, France

Abstract. The western Mediterranean region is prone to devastating flash-flood induced by heavy precipitation events (HPEs), which are responsible for considerable human and material damage. Quantitative precipitation forecasts have improved dramatically in recent years to produce realistic accumulated rainfall estimations. Nevertheless, challenging issues remain in reducing uncertainties in the initial conditions assimilation and the modeling of physical processes. In this study, the spatial errors resulting from a 30-year (1981–2010) ensemble hindcast which implement the same physical parametrizations as in the operational Météo-France short-range ensemble prediction system, Prévision d'Ensemble ARPEGE (PEARP), are analysed. The hindcast consists of a 10-member ensemble reforecast, run every 4-days, covering the period from September to December. 24-hour precipitation fields are classified in order to investigate the local variation of spatial properties and intensities of rainfall fields, with particular focus on the HPEs. The feature-based quality measure SAL is then performed on the model forecast and reference rainfall fields, which shows that both the amplitude and structure components are basically driven by the deep convection parametrization. Between the two main deep convection schemes used in PEARP, we qualify that the PCMT parametrization scheme performs better than the B85 scheme. A further analysis of spatial features of the rainfall objects to which the SAL metric pertains shows the predominance of large objects in the verification measure. It is for the most extreme events that the model has the best representation of the distribution of object integrated rain.

Matteo Ponzano et al.
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Short summary
We assess a methodology to evaluate and improve intense precipitation forecasting in the Southeastern French region. This methodology is based on the use of a 30-years dataset of past forecasts which are analysed using a spatial verification approach. We found that precipitation forecasting is qualitatively driven by the deep convection parametrization. Locally the model is able to reproduce the distribution of rainfall spatially integrated rainfall patterns of the most intense precipitations.
We assess a methodology to evaluate and improve intense precipitation forecasting in the...
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