A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes. Issue 7 (30th March 2023)
- Record Type:
- Journal Article
- Title:
- A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes. Issue 7 (30th March 2023)
- Main Title:
- A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes
- Authors:
- Mereu, L.
Scollo, S.
Garcia, A.
Sandri, L.
Bonadonna, C.
Marzano, F. S. - 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. 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 eruptionAbstract: 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 … (more)
- Is Part Of:
- Geophysical research letters. Volume 50:Issue 7(2023)
- Journal:
- Geophysical research letters
- Issue:
- Volume 50:Issue 7(2023)
- Issue Display:
- Volume 50, Issue 7 (2023)
- Year:
- 2023
- Volume:
- 50
- Issue:
- 7
- Issue Sort Value:
- 2023-0050-0007-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2023-03-30
- Subjects:
- explosive eruptions -- X ‐band radar observations -- eruption source parameters -- MCMC parametric model -- uncertainty estimation -- time series statistical analysis
Geophysics -- Periodicals
Planets -- Periodicals
Lunar geology -- Periodicals
550 - Journal URLs:
- http://www.agu.org/journals/gl/ ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1029/2022GL100596 ↗
- Languages:
- English
- ISSNs:
- 0094-8276
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4156.900000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26894.xml