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Savona, Italy

Parodi A.,CIMA Research Foundation | Tanelli S.,Jet Propulsion Laboratory
Journal of Geophysical Research: Atmospheres

In this work, deep moist convective processes, observed during the Tropical Composition, Cloud and Climate Coupling Experiment (TC4) over the East Pacific Intertropical Convergence Zone, were modeled by means of high-resolution numerical simulations with the Weather Research and Forecasting model. Three different turbulence parameterizations and two microphysical parameterizations are used. Their impact on the spatio-temporal structure of predicted convective fields is compared to TC4 observations from a geostationary imager, airborne precipitation radar, and dropsondes. It is found that the large-eddy simulation turbulence closure "upscaled" to the terra incognita range of grid spacings (i.e., 0.1-1 km) is best suited to model the deep convective processes under examination. © Copyright 2010 by the American Geophysical Union. Source

Ghizzoni T.,Corporate Underwriting Geo Risks | Roth G.,University of Genoa | Rudari R.,CIMA Research Foundation
Journal of Hydrology

This contribution presents an assessment of the joint probability distribution able to describe multi-site multi-basin flood scenarios in a high dimensionality framework. This goal will be pursued through two different approaches: the multivariate skew-t distribution and the Student copula with arbitrary margins. While copulas have been widely used in the modeling of hydrological processes, the use of the skew-t distribution in hydrology has been only recently proposed with reference to a trivariate application (Ghizzoni et al., 2010, Adv. Water Resour., 33, 1243-1255). Both methods are here applied and discussed in a context of considerably higher dimensionality: the Upper Mississippi River floods. In fact, to enhance the characteristics of the correlation structure, eighteen nested and non-nested gauging stations were selected, with significantly different contributing areas. Such conditions represent a challenge for both the skew-t and the copula approach. In perspective, the ability of such approaches in explaining the multivariate aspects of the relevant processes is needed to specify flood hazard scenarios in terms of their intensity, extension and frequency. When this is associated to the knowledge of location, value and vulnerability of exposed elements, comprehensive flood risk scenarios can be produced, and risk cumuli quantified, for given portfolios, composed of wherever located risks. © 2011 Elsevier B.V. Source

Parodi A.,CIMA Research Foundation | Foufoula-Georgiou E.,University of Minnesota | Emanuel K.,Massachusetts Institute of Technology
Journal of Geophysical Research: Atmospheres

Previous studies have suggested that the statistical multiscale structure of rainfall can be parameterized in terms of thermodynamic descriptors of the storm environment, and such dependence has been successfully implemented in downscaling applications. In this paper we suggest that it is possible to adopt the raindrop terminal velocity as a physical parameter to explain to a large degree the statistical variability of convective rainfall over a range of scales. We examine this assertion by analysis of high-resolution simulations of an atmosphere in radiative-convective equilibrium performed using the Weather Research and Forecasting (WRF) model and prescribing different rain terminal velocity settings corresponding to small, slowly falling drops and large, quickly falling drops, respectively. The analysis has focused on the study of the dependence of some basic statistics of rainfall fields (probability distribution of convective rain cell areas, power spectra, and multiscale statistics of rainfall intensity) on the raindrop terminal velocity by using a well-documented and widely used atmospheric model. Possible applications of our results include downscaling of rainfall satellite measurements, conditional on limited microphysical information from dual-frequency spaceborne radars, and conversion of radar reflectivity to rain rate, conditional on drop size distribution inferred from the scaling parameters of the reflectivity fields. Copyright 2011 by the American Geophysical Union. Source

Molini L.,CIMA Research Foundation | Parodi A.,CIMA Research Foundation | Rebora N.,CIMA Research Foundation | Craig G.C.,Ludwig Maximilians University of Munich
Quarterly Journal of the Royal Meteorological Society

Raingauge data over Italy for the period January 2006-February 2009 have been used to classify severe rainfall events into two types using a recently developed methodology. The types are defined as either long-lived and spatially distributed (Type I) if lasting more than 12 h and larger than 50 × 50 km2 or brief and localized (Type II) if having shorter duration or smaller spatial extent. A total of 81 events were identified, with 51 classified as Type I and 30 as Type II. The work presented here examines the hypothesis that the two types of event are associated with different dynamical regimes distinguished by differing degrees of control of convective precipitation by the synoptic-scale flow. For each of the 81 events, a time-scale for convective adjustment is computed, based on gridded hourly precipitation rates derived from rain-gauge data and ECMWF analysis (ERA-Interim) of convective available potential energy (CAPE). Values of the convective adjustment time-scale, τc, shorter than 6 h indicate convection that is responding rapidly to to the synoptic environment (equilibrium), while slower time-scales indicate that other, presumably local, factors dominate. It was anticipated that τc > 6 h would correspond to brief and localized Type II events, while τc < 6 h would indicate Type I events. This hypothesis was largely confirmed, with 45 of the 51 Type I events having time-scales shorter than 6 h and 20 of the 30 Type II events having time-scales longer than 6 h. © 2011 Royal Meteorological Society. Source

Silvestro F.,CIMA Research Foundation | Rebora N.,CIMA Research Foundation
Journal of Hydrology

One of the main difficulties that flood forecasters are faced with is evaluating how errors and uncertainties in forecasted precipitation propagate into streamflow forecast. These errors, must be combined with the effects of different initial soil moisture conditions that generally have a significant impact on the final results of a flood forecast. This is further complicated by the fact that a probabilistic approach is needed, especially when small and medium size basins are considered (the variability of the streamflow scenarios is in fact strongly influenced by the aforementioned factors). Moreover, the ensemble size is a degree of freedom when a precipitation downscaling algorithm is part of the forecast chain. In fact, a change of ensemble size could lead to different final results once the other inputs and parameters are fixed. In this work, a series of synthetic experiments have been designed and implemented to test an operational probabilistic flood forecast system in order to augment the knowledge of how streamflow forecasts can be affected by errors and uncertainties associated with the three aforementioned elements: forecasted rainfall, soil moisture initial conditions, and ensemble size. © 2014 The Authors. Source

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