HOTVOLC: the official French satellite-based service for operational monitoring and early warning of volcanic ash plumes

Abstract

Early detection of volcanic ash clouds is crucial to aviation safety and airspace surveillance. With the increase in air traffic and the frequency of volcanic eruptions, the need for effective warning procedures and improved detection methods has become obvious. The eruption of the Eyjafjallajökull volcano in 2010 showed that air traffic operations were severely disrupted and highlighted the importance of effective communication strategies between stakeholders. To improve monitoring capabilities, satellite techniques have become essential due to their wide coverage and rapid response. This article presents the HOTVOLC 3.0 web-Geographic Information System (GIS) interface, an enhanced version of the French operational monitoring platform developed at the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC) and certified by the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS-INSU). By integrating data from the Meteosat Second Generation (MSG) satellites, HOTVOLC 3.0 enables early detection of volcanic activity and monitoring of ash plumes and clouds. The HOTVOLC service is designed to support volcanic observatories, volcano research institutes, Volcanic Ash Advisory Centers (VAAC), and other operational actors who play a crucial role in decision-making and the implementation of effective risk management strategies for aviation safety. After a description of how the system works, we provide details of the updated web interface, which enhances the user experience by offering an interface with an operational mode and an archive mode, enabling easy access to past eruptions for training purposes. In the second part, we look at the various ash-related observation products (detection algorithms and quantitative products) that are disseminated via the new interface. Finally, we explore future developments of the platform, including the use of machine learning for ash detection, the integration of data from other geostationary satellites to improve product quality, and the forthcoming arrival of data from Meteosat Third Generation (MTG) satellites.