A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes
Luigi Mereu, Simona Scollo, Alexander García-Aristizábal, Laura Sandri, Costanza Bonadonna, Frank S. Marzano
Abstract
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.