ALBERT

Description: A fundamental feature of any earthquake early warning system is the ability of rapidly broadcast earthquake information to reach a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the Internet and represent the ideal tools for earthquake alerts dissemination to inform a large number of users about the potential damaging shaking of an impending earthquake. Here we present a mobile app (named ISNet EWApp or simply EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EEWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in southern Italy. In the absence of alerts, EWApp displays the standard bulletin of seismic events that have occurred within the network. In the event of a relevant earthquake, the app has a dedicated module to predict the expected ground-shaking intensity and the available lead time at the user's position and to provide customized messages to inform the user about the proper reaction to adopt during the alert. We first present the architecture of both the network-based system and EWApp and then describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.

Description: River discharges are the main expression of the hydrological cycle and are the results of climate natural variability. The signal of climate changes occurrence raises the question of how it will impact on river flows and on their extreme manifestations: floods and droughts. This question can be addressed through numerical simulations spanning from the past (1971) to future (2100) under different climate change scenarios. This work addresses the capability of a modelling chain to reproduce the observed discharge of the Po River over the period 1971–2000. The modelling chain includes climate and hydrological/hydraulic models and its performance is evaluated through indices based on the flow duration curve. The climate datasets used for the 1971–2000 period are (a) a high resolution observed climate dataset, and COSMO-CLM regional climate model outputs with (b) perfect boundary condition, ERA40 Reanalysis, and (c) suboptimal boundary conditions provided by the global climate model CMCC–CM. The aim of the different simulations is to evaluate how the uncertainties introduced by the choice of the regional and/or global climate models propagate in the simulated discharges. This point is relevant to interpret the results of the simulated discharges when scenarios for the future are considered. The hydrological/hydraulic components are simulated through a physically-based distributed model (TOPKAPI) and a water balance model at the basin scale (RIBASIM). The aim of these first simulations is to quantify the uncertainties introduced by each component of the modelling chain and their propagation. Estimation of the overall uncertainty is relevant to correctly understand the future river flow regimes. The results show how bias correction algorithms can help in reducing the overall uncertainty associated to the different stages of the modelling chain.

Description: In this study we compare three gridded observed datasets of daily precipitation (EOBS, MAP and NWIOI) over the Great Alpine Region (GAR) and a subregion in northwest Italy (NWI) in order to better understand the past variability of daily climate extremes and to set up a basis for developing regional climate scenarios. The grids are first compared with respect to their temporal similarity by calculating the correlation and relative mean absolute error of the time series. They are then compared with respect to their spatial similarity to the climatology of the ETCCDI indices (characterizing total precipitation, dry and wet spells and extremes with short return periods). The results indicate first that most EOBS gridpoint series in northeastern Italy have to be shifted back by 1 day to have maximum overlap of the measurement period and, second, that both the temporal and spatial similarities of most indices are higher between the NWIOI and MAP than between MAP or the NWIOI and EOBS. These results suggest that, although there is generally good temporal agreement between the three datasets, EOBS should be treated with caution, especially for extreme indices over the GAR region, and it does not provide reliable climatology over the NWI region. The high agreement between MAP and NWIOI, on the other hand, builds confidence in using these datasets. Users should consider carefully the limitations of the gridded observations available: the uncertainties of the observed datasets cannot be neglected in the overall uncertainties cascade that characterizes climate change studies.

Description: A fundamental feature of any Earthquake Early Warning System is the ability of rapidly broadcast earthquake information to a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the internet and represent the ideal tools for earthquake alerts dissemination, to inform a large number of users about the potential damaging shaking of an impending earthquake. Here we present a mobile App (named ISNet EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in Southern Italy. In the absence of alerts, the EWApp displays the standard bulletin of seismic events occurred within the network. In the event of a relevant earthquake, instead, the app has a dedicated module to predict the expected ground shaking intensity and the available lead-time at the user position and to provide customized messages to inform the user about the proper reaction during the alert. We first present the architecture of both network-based system and EWApp, and then and describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.

Description: The region of central and eastern Europe is an area characterised by a relatively high seismic risk. Since 2001, to monitor the seismicity of this area, the OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Italy, the Agencija Republike Slovenije za Okolje (ARSO) in Slovenia, the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Austria, and the Università di Trieste (UniTS) have cooperated in real-time seismological data exchange. In 2014 OGS, ARSO, ZAMG and UniTS created a cooperative network named the Central and Eastern European Earthquake Research Network (CE3RN), and teamed up with the University of Naples Federico II, Italy, to implement an earthquake early warning system based on the existing networks. Since May 2014, the earthquake early warning system (EEWS) given by the integration of the PRESTo (PRobability and Evolutionary early warning SysTem) alert management platform and the CE3RN accelerometric stations has been under real-time testing in order to assess the system's performance. This work presents a preliminary analysis of the EEWS performance carried out by playing back real strong motion recordings for the 1976 Friuli earthquake (MW= 6.5). Then, the results of the first 6 months of real-time testing of the EEWS are presented and discussed.

Description: Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.