NERC Data Assimilation Training

2014 Data assimilation and visualisation in environmental sciences - an advanced training course

15-19 September, University of Reading
This 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
Fully funded places are available.
Below is a list of the given talks and auxiliary material provided.
General Information
Course programme
Mathematics and statistics primer
Group photo
Lectures and practicals
Amos Lawless
Lecture 1: Introduction to data assimilation
Ross Bannister
Lecture 2: Further introduction - including covariance functions
Polly Smith
Practical 1: Covariance functions in a 1D system
Phil Browne
Lecture 3:: Introduction to models and Archer
Nancy Nichols
Lecture 4:: Theory of variational data assimilation
Polly Smith
Practicals 2/3: Variational methods
Amos Lawless
Lecture 5: Variational assimilation - practical considerations
Alison Fowler
Lecture 6: Observation impact
Sarah Dance
Lecture 7: Ensemble Kalman Filter theory
Sanita Vetra-Carvalho
Practical 4: Ensemble Kalman Filter
Jon Blower and Debbie Clifford
Lecture 8: Visualization: some principles and examples
Jon Blower and Debbie Clifford
Practical 5: Visualization
Ross Bannister
Lecture 9: Ensemble Kalman Filter practical considerations
Peter Jan van Leeuwen
Lecture 10: Particle filters and Markov chain Monte-Carlo
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
Tristan Quaife
Lecture 12: Data assimilation for a carbon cycle model
Tristan Quaife
Practical 7: Ensemble Kalman filter and particle filter using DALEC carbon cycle model
Peter Jan van Leeuwen
Lecture 13: Summary of data assimilation methods and what to use when
Background error covariance matrix for a tropospheric temperature profile

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