Dr. Helen Webster

IDSAI Seminar: Using ensemble meteorological datasets to overcome limitations in a Bayesian volcanic ash inverse modelling system

Open to University of Exeter staff and students

An Institute for Data Science and Artificial Intelligence seminar
Date22 February 2021
Time15:00 to 16:00
PlaceOnline

Join Dr Helen Webster, lecturer in Global Systems and Senior Scientist at the Met Office, as she discusses using ensemble meteorological datasets to overcome limitations in a Bayesian volcanic ash inverse modelling system

Volcanic ash in the atmosphere poses a significant hazard to aviation. To minimise risk, atmospheric dispersion models are used to predict the transport of ash clouds. Accurate ash cloud forecasts require a good estimate of mass eruption rates and ash injection heights. These parameters are, however, highly uncertain and inversion techniques have been developed to better constrain the emission source term and improve ash cloud forecasts. 

InTEM for volcanic ash is a Bayesian inversion method which obtains a best estimate of height- and time-varying ash emission rates. It combines satellite observations of the ash cloud, prior estimates of the ash emissions and an atmospheric dispersion model. Uncertainties in the atmospheric dispersion model, including uncertainty in the driving meteorological data, are not currently represented and this limits the success of the method when such errors are significant. 

Discrepancies between modelled and observed ash clouds from the 2011 eruption of the Icelandic volcano Grímsvötn were previously attributed to errors in the input meteorological data. We use this eruption as a case study to investigate using an ensemble of numerical weather prediction forecasts to improve ash cloud forecasts by accounting for meteorological errors in InTEM. An iterative method is employed to identify the best meteorological dataset. We explore if improvements are seen and how this method might be implemented in an operational context. 

Register for the seminar here.
 


Abstract


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