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

Matteo Ponzano, Bruno Joly, Laurent Descamps, Philippe Arbogast
2019 unpublished
<p><strong>Abstract.</strong> 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
more » ... processes. In this study, the spatial errors resulting from a 30-year (1981&amp;ndash;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.</p>
doi:10.5194/nhess-2019-302 fatcat:tihhm7h2vfakdmwsc3r4faw66m