Articles | Volume 12, issue 10
Nat. Hazards Earth Syst. Sci., 12, 2993–3011, 2012

Special issue: Forecast and projection in climate scenario of Mediterranean...

Nat. Hazards Earth Syst. Sci., 12, 2993–3011, 2012

Research article 01 Oct 2012

Research article | 01 Oct 2012

Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events

O. Nuissier, B. Joly, B. Vié, and V. Ducrocq O. Nuissier et al.
  • GAME/CNRM, Météo-France, URA1357, CNRS, Toulouse, France

Abstract. This study examines the impact of lateral boundary conditions (LBCs) in convection-permitting (C-P) ensemble simulations with the AROME model driven by the ARPEGE EPS (PEARP). Particular attention is paid to two torrential rainfall episodes, observed on 15–16 June 2010 (the Var case) and 7–8 September 2010 (the Gard-Ardèche case) over the southeastern part of France. Regarding the substantial computing time for convection-permitting models, a methodology of selection of a few LBCs, dedicated for C-P ensemble simulations of heavy precipitation events is evaluated. Several sensitivity experiments are carried out to evaluate the skill of the AROME ensembles, using different approaches for selection of the driving PEARP members. The convective-scale predictability of the Var case is very low and it is driven primarily by a surface low over the Gulf of Lyon inducing a strong convergent low-level flow, and accordingly advecting strong moisture supply from the Mediterranean Sea toward the flooded area. The Gard-Ardèche case is better handled in ensemble simulations as a surface cold front moved slowly eastwards while increasing the low-level water vapour ahead is well reproduced. The selection based on a cluster analysis of the PEARP members generally better performs against a random selection. The consideration of relevant meteorological parameters for the convective events of interest (i.e. geopotential height at 500 hPa and horizontal moisture flux at 925 hPa) refined the cluster analysis. It also helps in better capturing the forecast uncertainty variability which is spatially more localized at the "high-impact region" due to the selection of more mesoscale parameters.