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| Titel |
Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events |
| VerfasserIn |
O. Nuissier, B. Joly, B. Vié, V. Ducrocq |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 10 ; Nr. 12, no. 10 (2012-10-01), S.2993-3011 |
| Datensatznummer |
250011139
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| Publikation (Nr.) |
 copernicus.org/nhess-12-2993-2012.pdf |
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| Zusammenfassung |
| 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. |
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