A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes. Issue 7 (30th March 2023) - Mereu, L.; Scollo, S.; Garcia, A.; Sandri, L.; Bonadonna, C.; Marzano, F. S.
Abstract: Accurate forecasting of volcanic particle (tephra) dispersal and fallout requires a reliable estimation of key Eruption Source Parameters (ESPs) such as the Mass Eruption Rate ( Q M ). Q M is usually estimated from the Top Plume Height ( H TP ) using empirical and analytical models. For the first time, we combine estimates of H TP and Q M derived from the same sensor (radar) with mean wind velocity values ( v W ) for lava‐fountain fed tephra plumes associated with 32 paroxysms of Mt. Etna (Italy) to develop a new statistical model based on a Markov Chain Monte Carlo approach for model parameter estimation. This model is especially designed for application to radar data to quickly infer Q M from observed H TP and v W, and estimate the associated uncertainty. It can be easily applied and adjusted to data retrieved by radars worldwide, improving our capacity to quickly estimate Q M and related uncertainties required for the tephra dispersal hazard. Plain Language Summary: New radar‐based statistical model useful to quickly infer the mass eruption rate, usually the key parameter to initialize the tephra dispersion model, from the volcanic plume height during the eruptions. Key Points: X‐band radar observations of explosive eruptions can be used to estimate the eruption source parameters and associated uncertainties Using the Markov Chain Monte Carlo model can be performed the statistical analysis of time series Statistical parametric model to infer the mass eruption rate from the measured top plume height