An ensemble study of HyMeX IOP6 and IOP7a: Sensitivity to physical and initial and boundary condition uncertainties

Abstract

The first Special Observation Period of the HyMeX campaign took place in the Mediterranean between September and November 2012 with the aim of better understanding the mechanisms which lead to heavy precipitation events (HPEs) in the region during the autumn months. Two such events, referred to as Intensive Observation Period 6 (IOP6) and Intensive Observation Period 7a (IOP7a), occurred respectively on 24 and 26 September over south-eastern France. IOP6 was characterised by moderate to weak low-level flow which led to heavy and concentrated convective rainfall over the plains near the coast, while IOP7a had strong low-level flow and consisted of a convective line over the mountainous regions further north and a band of stratiform rainfall further east. Firstly, an ensemble was constructed for each IOP using analyses from the AROME, AROME-WMED, ARPEGE and ECMWF operational models as initial (IC) and boundary (BC) conditions for the research model Meso-NH at a resolution of 2.5 km. A high level of model skill was seen for IOP7a, with a lower level of agreement with the observations for IOP6. Using the most accurate member of this ensemble as a CTRL simulation, three further ensembles were constructed in order to study uncertainties related to cloud physics and surface turbulence parameterisations. Perturbations were introduced by perturbing the time tendencies of the warm and cold microphysical and turbulence processes. An ensemble where all three sources of uncertainty were perturbed gave the greatest degree of dispersion in the surface rainfall for both IOPs. Comparing the level of dispersion to that of the ICBC ensemble demonstrated that when model skill is low (high) and low-level flow is weak to moderate (strong), the level of dispersion of the ICBC and physical perturbation ensembles is (is not) comparable. The level of sensitivity to these perturbations is thus concluded to be case dependent. © Author(s) 2014.