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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 (QM). QM is usually estimated from the Top Plume Height (HTP) using empirical and analytical models. For the first time, we combine estimates of HTP and QM derived from the same sensor (radar) with mean wind velocity values (vW) 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 QM from observed HTP and vW, and estimate the associated uncertainty. It can be easily applied and adjusted to data retrieved by radars worldwide, improving our capacity to quickly estimate QM 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 uncertaintiesUsing the Markov Chain Monte Carlo model can be performed the statistical analysis of time seriesStatistical parametric model to infer the mass eruption rate from the measured top plume height [ABSTRACT FROM AUTHOR]