UQ in earth sciences: applications and challenges
57 mins 7 secs,
828.23 MB,
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| Description: |
Lee, L (University of Leeds)
Tuesday 9th January 2018 - 13:30 to 14:30 |
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| Created: | 2018-01-10 16:20 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Lee, L |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Ken Carslaw (University of Leeds), Carly Reddington (University of Leeds), Kirsty Pringle (University of Leeds), Graham Mann (University of Leeds), Oliver Wild (University of Lancaster), Edmund Ryan (University of Lancaster), Philip Stier (University of Oxford), Duncan Watson-Parris (University of Oxford)
I will introduce some of the applications of UQ in earth sciences and the challenges remaining that could be addressed during the programme. Earth science models are 3-d dynamic models whose CPU demands and data storage often limits the sample size for UQ. We often choose to use averages of the data and dimension reduction to carry out UQ but it is not always clear that the uncertainty quantified is the most useful for uncertainty reduction or increasing confidence in prediction. I will ask whether we should be applying the same techniques to understand and improve the model as those used to reduce uncertainty in predictions showing some examples where the end goal is different. I will look at UQ when constraint or calibration is the goal and how we incorporate uncertainty and use ‘real’ data. This will also raise the question of identifiability in our uncertainty quantification and how to deal with and accurately quantify irreducible uncertainty. Finally, I would like to discuss how we validate our methods in a meaningful way. |
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