During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide. medium lapilli has been widely considered as a primary risk agent related to explosive volcanic activity, fallout of coarse lapilli to small blocks falling from plume margins has been underrated. As an example, during the event at Etna on 23 November 2013, clasts from several centimeters to decimeters fell within 5-6 km from the summit and hit hikers who were in the touristic areas [8]. Although the assessment of tephra fallout and dispersal in distal areas has been largely considered [9][10][11][12], the reduction of volcanic impacts in proximal areas and within the first hour from the beginning of the eruption is still a challenge. As a matter of fact, regardless of the importance of this information for emergency responders and government agencies, the operational systems capable of monitoring tephra dispersal and fallout in near-real-time (NRT) and returning the expected impact assessment are still limited and not fully adapted to the growing requirements of precision and reliability.A good example of NRT tephra detection in volcano observatories is represented by the Alaska Volcano Observatory (AVO), which monitors volcanoes within the North Pacific region [13]. The AVO system analyzes data from different satellite sensors. They use a 24/7 automated ash cloud detection algorithm that sends emails and phone text alerts to the AVO members, who are, in turn, responsible for verifying if the automatic alert can be considered as true or false [13]. The Kamchatka Volcanic Eruption Response Team (KVERT) monitors 36 active volcanoes in ...

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Real‐time assessment of the state of a volcano plays a key role for civil protection purposes. Unfortunately, because of the coupling of highly nonlinear and partially known complex volcanic processes, and the intrinsic uncertainties in measured parameters, the state of a volcano needs to be expressed in probabilistic terms, thus making any rapid assessment sometimes impractical. With the aim of aiding on‐duty personnel in volcano‐monitoring roles, we present an expert system approach to automatically estimate the ongoing state of a volcano from all available measurements. The system consists of a probabilistic model that encodes the conditional dependencies between measurements and volcanic states in a directed acyclic graph and renders an estimation of the probability distribution of the feasible volcanic states. We test the model with Mount Etna (Italy) as a case study by considering a long record of multivariate data. Results indicate that the proposed model is effective for early warning and has considerable potential for decision‐making purposes.