NERC Data Assimilation Training
2014 Data assimilation and visualisation in environmental sciences - an advanced training course
15-19 September, University of ReadingThis course was aimed at PhD students and early career researchers and included lectures and computer practucals on the following topics
- Introduction to the basics of data assimilation
- Variational data assimilation
- ensemble Kalman filters and hybrid methods
- particle filters and Markov Chain Monte-Carlo methods
- data visualisation
Below is a list of the given talks and auxiliary material provided.
| General Information | ||
| Course programme | 
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| Mathematics and statistics primer | 
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| Group photo | 
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| Lectures and practicals | ||
| Amos Lawless Lecture 1: Introduction to data assimilation |
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| Ross Bannister Lecture 2: Further introduction - including covariance functions |
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| Polly Smith Practical 1: Covariance functions in a 1D system |
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| Phil Browne Lecture 3:: Introduction to models and Archer |
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| Nancy Nichols Lecture 4:: Theory of variational data assimilation |
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| Polly Smith Practicals 2/3: Variational methods |
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| Amos Lawless Lecture 5: Variational assimilation - practical considerations |
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| Alison Fowler Lecture 6: Observation impact |
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| Sarah Dance Lecture 7: Ensemble Kalman Filter theory |
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| Sanita Vetra-Carvalho Practical 4: Ensemble Kalman Filter |
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| Jon Blower and Debbie Clifford Lecture 8: Visualization: some principles and examples |
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| Jon Blower and Debbie Clifford Practical 5: Visualization |
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| Ross Bannister Lecture 9: Ensemble Kalman Filter practical considerations |
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| Peter Jan van Leeuwen Lecture 10: Particle filters and Markov chain Monte-Carlo |
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| Peter Jan van Leeuwen Lecture 11: Particle filters and Markov chain Monte-Carlo (continued) |
As above. | |
| Phil Browne Practical 6:: Particle filters and Markov chain Monte-Carlo |
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| Tristan Quaife Lecture 12: Data assimilation for a carbon cycle model |
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| Tristan Quaife Practical 7: Ensemble Kalman filter and particle filter using DALEC carbon cycle model |
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| Peter Jan van Leeuwen Lecture 13: Summary of data assimilation methods and what to use when |
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