A high-resolution ensemble system, based on five runs of a limited-area model (LAM), is described. The initial and boundary conditions for the LAM integrations are provided by the representative members (RMs) selected from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (EPS). @S meinbeax are grouped in five clusters; then, from each cluster, an RM is selected, according to the methodology described in the companion paper. The ability of the high-resolution ensemble system to predict the occurrence of heavy rainfall events (either five or six days ahead) is tested for four cases of floods over the Alpine region. Results show that, in two case-studies, the LAM integration corresponding to the RM of the highly populated cluster predicts the observed rainfall with a very good degree of time and spatial accuracy. In the other two cases, the extreme events are captured by at least one of the runs nested on the members of the less populated clusters. Probability maps constructed from LAM integrations provide great detail on the location of the regions affected by heavy precipitation and the information gained with respect to EPS probability maps and LAM deterministic forecasts is highlighted. The probabilistic estimates based on the LAM ensembles are also shown to be of valuable assistance to forecasters in issuing early flood alerts, contributing to the definition of a flood-risk alarm system. t In November 2000, the horizontal resolution of the operational EPS was increased to T~2 5 5 , Corresponding to a grid scale of approximately 80 km. @ Royal Meteorological Society, 2001. 209s 2096 C. MARSIGLI et al.and boundary conditions provided by the representative members (hereafter, RMs) of the ECMWF EPS. The RMs are selected first by applying a cluster analysis to the 51member EPS to define five clusters and, then, by identifying the RM of each cluster. Clusters are defined by considering the atmospheric flow at 700 hPa and by using the wind vector as clustering variable. Once the five clusters have been constructed, for each cluster the RM is defined as the member closest to all members of its own cluster and most distant from the members of the other clusters, with distances computed using an Ll norm applied to the precipitation field. The reader is referred to the companion paper, Molteni et al. (2001), and to Marsigli (1998) for a detailed description of the selection methodology. LEPS is based on integrations of the limitedarea model LAMBO (Limited Area Model Bologna), operational at ARPA-SMR since 1993. LAMBO runs are performed at high horizontal resolution (about 20 km) in order to resolve those orographic and mesoscale processes responsible for heavy -precipitation events. A probability of occurrence is assigned to each scenario, based on the population of the corresponding EPS cluster. In this way, it is possible to combine the ability of the EPS to highlight a set of possible evolution scenarios (keeping account of the intrinsic predictability of a particular synoptic situation...

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AbstractmLight partition has been examined and evaluated on five woody species (Olea europaea, Ficus carica, Pittosporum tobira, Hedera helix maculata, Persica vulgaris) in relation to their leaf morpho-histological characteristics, water and chlorophyll contents. Leaf parameters and optical properties (reflectance, transmittance, absorbance) in PAR, FR and NIR wavebands (400±1100 nm) were preliminarily submitted to a canonical correlation analysis where lamina thickness and water content showed a leading role in determining all the optical properties, while chlorophyll, influential in the PAR region, was remarkably effective only in an extreme pigment situation when green and albino patches of ivy leaves were compared. Transmittance appeared inversely related to lamina thickness in accordance with the Lambert Beer law. Significant correlations were found also between mesophyll water content and both transmittance (positive) and reflectance (negative). Olive leaves showed peculiar optical patterns because of the dense and continuous trichome layer on their abaxial surface.

In the last few years, tens of alternative weather forecasts have been made available to forecasters by operational ensemble prediction systems. In many forecasting applications, it is useful to identify (possibly in an objective way) a few representative ensemble members, deemed to represent the most interesting weather scenarios. In this paper, a strategy to select representative members (RMs hereafter) from an ensemble prediction is developed, and applied to four cases of medium-range ensemble forecasts performed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS). The four casestudies correspond to events of very intense rainfall (leading to localized floods) in the Alpine region, selected as benchmarks for numerical simulations in the Mesoscale Alpine Programme. The RM selection procedure uses a cluster analysis of the ensemble forecasts as its first step. For each cluster, an RM is defined to be the member with the smallest ratio between its average distance from the members of its own cluster and its average distance from the members of the other clusters. Distances are computed either using an L2-norm applied to 700 hPa geopotential height fields or an L1-norm to precipitation fields.RMs are compared with cluster centroids in the four case-studies of extreme rainfall. By definition, RMs are characterized by a synoptic-scale atmospheric flow similar to the flow of the corresponding cluster centroid, but they contain more small-scale features, especially in the prediction of weather parameters such as precipitation.RM initial conditions can be used to initiate higher-resolution global forecasts; alternatively, RMs may be used to define initial and boundary conditions for nested high-resolution forecasts with limited-area models. Integrations of RMs with the ECMWF global model at Tl319 horizontal resolution (compared with the TI 159 resolution used in the EPS) were performed. Results indicate that each higher-resolution forecast, started from RM initial conditions, remains closer to the low-resolution RM than to other ensemble members, but provides a more detailed forecast of weather parameters, especially in regions of complex topography. Experiments with a nested limited-area model, started from the same set of RMs, are described in a companion paper.

Abstract. The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so as to generate a highly-populated "super-ensemble". Then, five members are selected from the super-ensemble and used to provide initial and boundary conditions for the integrations with a limited-area model, whose runs generate a Limitedarea Ensemble Prediction System (LEPS). The relative impact of targeting the initial perturbations against increasing the horizontal resolution is assessed for the global ensembles as well as for the properties transferred to LEPS integrations, the attention being focussed on the probabilistic prediction of rainfall over a localised area. At the 108, 84 and 60-hour forecast ranges, the overall performance of the global ensembles is not particularly accurate and the best results are obtained by the high-resolution version of EPS. The LEPS performance is very satisfactory in all configurations and the rainfall maps show probability peaks in the correct regions. LEPS products would have been of great assistance to issue flood risk alerts on the basis of limited-area ensemble forecasts. For the 60-hour forecast range, the sensitivity of the results to the LEPS ensemble size is discussed by comparing a 5-member against a 51-member LEPS, where the limitedarea model is nested on all EPS members. Little sensitivity is found as concerns the detection of the regions most likely affected by heavy precipitation, the probability peaks being approximately the same in both configurations.