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SUMMARY:Using ensemble meteorological datasets to overcome limitations in 
 a Bayesian volcanic ash inverse modelling system - Helen Webster | Met Off
 ice
DTSTART:20210209T110000Z
DTEND:20210209T123000Z
UID:TALK155386@talks.cam.ac.uk
CONTACT:Tudor Suciu
DESCRIPTION:Volcanic ash in the atmosphere poses a significant hazard to a
 viation. To minimise risk\, atmospheric dispersion models are used to pred
 ict the transport of ash clouds. Accurate ash cloud forecasts require a go
 od estimate of mass eruption rates and ash injection heights. These parame
 ters are\, however\, highly uncertain and inversion techniques have been d
 eveloped to better constrain the emission source term and improve ash clou
 d forecasts.\nInTEM for volcanic ash is a Bayesian inversion method which 
 gives a best estimate of height- and time-varying ash emission rates. It c
 ombines satellite observations of the ash cloud\, prior estimates of the a
 sh emissions and an atmospheric dispersion model. Uncertainties in the atm
 ospheric dispersion model\, including uncertainty in the driving meteorolo
 gical data\, are not currently represented and this limits the success of 
 the method when such errors are significant.\nDiscrepancies between modell
 ed and observed ash clouds from the 2011 eruption of the Icelandic volcano
  Grímsvötn were previously attributed to errors in the input meteorologi
 cal data. We use this eruption as a case study to investigate using an ens
 emble of numerical weather prediction forecasts to improve ash cloud forec
 asts by accounting for meteorological errors in InTEM. An iterative method
  is employed to identify the best meteorological dataset. We explore if im
 provements are seen and how this method might be implemented in an operati
 onal context.\n\n\nJoin Zoom Meeting\nhttps://zoom.us/j/6708259482?pwd=Qk0
 3U3hxZWNJZUZpT2pVZnFtU2RRUT09\n\nMeeting ID: 670 825 9482\nPasscode: 9fkTA
 c
LOCATION:https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
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