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Abstract. It is widely recognised that an effective exploitation of Information and Communication Technologies (ICT) is an enabling factor to achieve major advancements in Hydro-Meteorological Research (HMR). Recently, a lot of attention has been devoted to the use of ICT in HMR activities, e.g. in order to facilitate data exchange and integration, to improve computational capabilities and consequently model resolution and quality. Nowadays, ICT technologies have demonstrated that it is possible to extend monitoring networks by integrating sensors and other sources of data managed by volunteer's communities. These networks are constituted by peers that span a wide portion of the territory in many countries. The peers are "location aware" in the sense that they provide information strictly related with their geospatial location. The coverage of these networks, in general, is not uniform and the location of peers may follow random distribution. The ICT features used to set up the network are lightweight and user friendly, thus, permitting the peers to join the network without the necessity of specialised ICT knowledge. In this perspective it is of increasing interest for HMR activities to elaborate of Personal Weather Station (PWS) networks, capable to provide almost real-time, location aware, weather data. Moreover, different big players of the web arena are now providing world-wide backbones, suitable to present on detailed map location aware information, obtained by mashing up data from different sources. This is the case, for example, with Google Earth and Google Maps. This paper presents the design of a mashup application aimed at aggregating, refining and visualizing near real-time hydro-meteorological datasets. In particular, we focused on the integration of instant precipitation depths, registered either by widespread semi-professional weather stations and official ones. This sort of information has high importance and usefulness in decision support systems and Civil Protection applications. As a significant case study, we analysed the rainfall data observed during the severe flash-flood event of 4 November 2011 over Liguria region, Italy. The joint use of official observation network with PWS networks and meteorological radar allowed for the making of evident finger-like convection structure.

No abstract available ; Il fine di questo documento ? la definizione di strategie che permettano l'utilizzo di librerie parallele in un ambiente di programmazione strutturato Grid- Aware e basato su un modello a componenti, quale sar? ASSIST nelle future versioni. In particolare siamo interessati a librerie parallele il cui dominio specifico d'applicazione ? l'image processing; arricchendole delle politiche di ottimizzazione e l'insieme di possibilit? di calcolo che ASSIST fornisce. Le strade che ci proponiamo ci percorrere sono sostanzialmente due: l'integrazione in ambiente Assist di una libreria disponibile, presentata all'interno del documento, in modo da riuscire ad utilizzarne tutte le funzionalit? all'interno di programmi ASSIST; lo sviluppo si una libreria parallela per l'image processing nativa in ambiente ASSIST, che si basi sulla sua filosofia di programmazione e ne sfrutti tutte le potenzialit?. Per raggiungere tali obiettivi all'interno del documento vengono presentate e dettagliate differenti possibilit?, prendendo in considerazione i pro ed i contro, e lo stato dell'arte.

Abstract. The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (RainFARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus giving a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios (> 1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash flood early-warning perspective.

From 1970 to 2012, about 9,000 high-impact weather events were reported globally, causing the loss of 1.94 million lives and damage of $2.4 trillion (U.S. dollars). The scientific community is called to action to improve the predictive ability of such events and communicate forecasts and associated risks both to affected populations and to those making decisions. At the heart of this challenge lies the ability to have easy access to hydrometeorological data and models and to facilitate the necessary collaboration between meteorologists, hydrologists, and computer science experts to achieve accelerated scientific advances. Two European Union (EU)-funded projects, Distributed Research Infrastructure for Hydro-Meteorology (DRIHM) and DRIHM to United States of America (DRIHM2US), sought to help address this challenge by developing a prototype e-science environment providing advanced end-to-end services (models, datasets, and postprocessing tools), with the aim of paving the way to a step change in how scientists can approach studying these events, with a special focus on flood events in complex topographic areas. This paper describes the motivation and philosophy behind this prototype e-science environment together with certain key components, focusing on hydrometeorological aspects that are then illustrated through actionable research for a critical flash flood event that occurred in October 2014 in Liguria, Italy.