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.

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One of the main challenges of the 21 st century is represented by accurate weather predictions together with the estimate of extreme phenomena and their impacts on the environment and on the society. The key point of this challenge is to enable the acceleration of advances in hydrometeorological research, and to integrate these advances in the everyday forecasts thus improving the protection of civilians and of the environment. The DRIHMS (Distributed Research Infrastructure for Hydro-Meteorology Study) project suggests that a step forward in this direction lies on the ability to easily access hydrometeorological data, to share predictive models, and to facilitate the collaboration among different experts in this area. At this aim it is necessary the support of an einfrastructure permitting to deal with the massive amount of information needed and providing the adequate level of systems interoperability. These are the goals of the DRIMH, Distributed Research Infrastructure for HydroMeteorology, project presented hereafter.

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The use of Web technologies for the collection and visualization of geoscentific data has significantly increased the availability of free sensor data over the Internet. This work aims at designing a Web mashup for the aggregation of meteorological variables (precipitation, humidity, pressure, etc.) published on the Web by several weather networks and the rendering of query results through a graphic interface in a homogeneous way. The mashup approach is particularly suitable to provide an easy to develop and quickly deployed application capable to support HM scientists in their everyday activity. As a significant case study of the adoption of the tool, the authors consider the severe flash-flood event that occurred in fall 2011 in the Liguria region, Italy. To this end, they base their analysis on the aggregated rainfall data observed by an official and a personal weather network.