1-1-1-1-1-1-1-1-1-1-1-1-1-1 From: Melody Whitehead <M.Whitehead@xxxxxxxxxxxx> Dear Colleagues, Please consider submitting an abstract to the EGU General Assembly 2024 session NH2.1 Volcanic uncertainty: from data to forecasts https://urldefense.com/v3/__https://meetingorganizer.copernicus.org/EGU24/sessionprogramme/5238*__;Iw!!IKRxdwAv5BmarQ!dzDiLsir5QvfQWbWfpBzntzWbgUc5PnsLOLptR3APUGFDb63elPGcPWndaIfo09spkoMXL8SM2Zr_d1J$ <https://urldefense.com/v3/__https://meetingorganizer.copernicus.org/EGU24/sessionprogramme/5238*__;Iw!!IKRxdwAv5BmarQ!fUNaDcBeUXqScPHlaRo52Rosbcd08nFbQZVRZ5m9pF4z2e4-tngX745tC4RuioKkkSv_8AkkJkD-U8BnUTP3az2KFn2hQA$> *Abstract submission deadline: 10 January 2024* Volcanoes are complex systems with the potential for catastrophic impacts. Each volcano has unique and evolving characteristics, for which there are incomplete and temporally biased data. This uncertainty hinders the construction of robust and reliable models for forecasts of both eruptions, and their high-risk hazards. Uncertainty is frequently cited as a major problem in volcanic hazard analyses and a plethora of statistical methods have been developed to try to quantify uncertainty in both hazard modelling and eruption forecasting. The data underlying models treating the volcano system to model both eruption occurrence and hazard propagation is multi-scale, multi-dimensional and nonlinearly correlated. This limited and highly structured data is often not representative of the volcano's potential behaviour. Hence additional knowledge is often required to provide the causal links between multivariate data, and to extrapolate outside of the perceived bounds of existing data. We invite submissions that try to tackle these complex problems and provide robust forecast uncertainties; we particularly welcome contributions on: â?¢ pragmatic approaches to uncertainty estimation and propagation from observational data to forecast output, â?¢ ensemble forecasting and multi-forecasting suites, â?¢ stochastic dynamics that focus on temporal (and possibly spatial) evolution, â?¢ conceptual and theoretical models with ground-breaking potential, â?¢ agile methods with the potential to accommodate future, but as of yet unknown, data and knowledge types, â?¢ the use of analogue, surrogate, and synthetic data. See you in Vienna! Mark Bebbington (m.bebbington@xxxxxxxxxxxx) and Mel Whitehead ( M.Whitehead@xxxxxxxxxxxx), Massey University, NZ Andy Bell (A.Bell@xxxxxxxx), University of Edinburgh, UK 1-1-1-1-1-1-1-1-1-1-1-1-1-1 ------------------------------
