DARC/NCEO Data Assimilation Training course, Univ. of Reading, 2022

Outline

Link to the timetable below

Data assimilation is one of the core activities needed to deliver weather forecasts, and to help understand the environment.  This course will provide an introduction to data assimilation methods used in numerical weather prediction, including some of the latest developments. All methods will be derived from a common framework to allow a full overview of their assumptions and applicability. Furthermore, participants will get the opportunity to apply data assimilation methods to small numerical models, in order to obtain hands-on experience with the different methods.

  • When?
    • Tuesday 21st – Friday 24th June 2022.
  • Where?
    • University of Reading, UK (Covid restrictions permitting; an on-line course will be offered if in-person lectures are not possible).
    • A map of the campus is available here.
  • Who is it for?
    • This course is suitable for postgraduate students, post-doctoral researchers, and weather service employees who would like an introduction to, and a deeper understanding of, data assimilation.
  • How will the course be delivered?
    • The course will be a combination of lectures and computer practicals.
  • What topics will be covered?
    • Introduction to Data Assimilation
    • Co-variances
    • Variational Methods: Theory and practical issues
    • Computer practicals Variational methods
    • Ensemble Kalman Filter Methods: Theory and practical issues
    • Computer practicals Ensemble Kalman filters
    • Non-linear data assimilation and Particle Filters: Theory and practical issues
    • Computer practicals Particle filters
    • Hybrid Methods: Theory and practical issues
    • Computer practicals Hybrid methods
    • When to use which method
  • What prerequisites are required?
    • Some knowledge of basic calculus, statistics, and linear algebra.  Mathematical primers are available below.
  • Who will deliver the lectures?
    • Lectures will be delivered by members of DARC including: Alison Fowler, Amos Lawless, Javier Amezcua, Ross Bannister, and Sarah Dance.
  • How much will it cost to attend?
    • The course is free to attend.  Attendees will need to pay for their own travel, accommodation, and subsistence.
  • How will I run the practicals?
    • A PC and Python software will be provided.  You may alternatively run the practicals on your own laptop computer.
  • How do I apply?
    • To apply for a place, please send an email to Uzma Saeed (u.saeed@reading.ac.uk), with a short description of your current research interests (around 50-100 words) by Tuesday 24th May 2022.  Applications will be looked at as they arrive, until all the places have been allocated.

Supplementary Material

  • Campus map (web page)
  • Setting-up for the computer practicals (pdf)
  • Practicals – variational and ensemble Kalman filter (zip)
  • Practicals – hydrids (zip)
  • Practicals – Bayes theorem and particle filters (zip)
  • Maths primer (pdf)
  • Useful linear algebra (pdf)
  • Inner and outer products (pdf)
  • Covariances (pdf)
  • Basic statistical concepts (pdf)
  • Notation (pdf)
  • List of suggested references (pdf)

Course timetable and lecture/practical material

The course will be held in the Agriculture Building (Ag) on the main Whiteknights campus of the University of Reading (building 59 on the map). Registration and lectures will be in room 1L16 and practicals in the PC lab GL20.

Handouts will not be provided by the lecturers, but lectures can be downloaded via the on-line timetable below when they become available.

Information about the models used in the practicals

Tuesday 21st June 2022
09.00 09.30 Ag 1L16 Registration
09.30 09.45 Welcome & introduction pdf
09.45 10.40 Lecture: Introduction to data assimilation Amos Lawless pdf
10.40 11.00 Break
11.00 12.00 Lecture: Variational data assimilation I Ross Bannister pdf
12.10 13:00 Lecture: Variational data assimilation II Ross Bannister pdf
13:05 14:00 Lunch break
14.00 16.00 PC lab Ag GL20 Practical: Variational data assimilation github
instructions
16.00 16.20 Break
16.20 17.20 Ag 1L16 Lecture: Variational methods practicalities Amos Lawless pdf
17.30 Maths common room, 112 (building 4) Icebreaker reception
Wednesday 22nd June 2022
09.00 10.00 Ag 1L16 Lecture: Ensemble Kalman filter I Alison Fowler pdf
10.10 11.10 Lecture: Ensemble Kalman filter II Alison Fowler pdf
11.10 11.40 Break
11.40 12.40 Lecture: Dynamical systems and data assimilation Jochen Broecker pdf
12.45 14.00 Lunch break
14.00 16.00 PC lab Ag GL20 Practical: EnKF github (as above)
instructions (as above)
16.00 16.20 Break
16.20 17.20 Ag 1L16 Lecture: Applications lecture – Exploiting infrasound measurements Javier Amezcua pdf
Thursday 23 rd June 2022
09.00 10.00 Ag 1L16 Lecture: Hybrid methods I Javier Amezcua pdf
10.10 11.10 Lecture: Hybrid methods II Javier Amezcua pdf
11.20 11.40 Break
11.40 12.40 Lecture: Machine learning and data assimilation Jochen Broecker pdf
12.45 14.00 Lunch break
14.00 16.00 PC lab Ag GL20 Practical: Hybrid methods github
instructions (as above)
16.00 16.20 Break
16.20 17.20 Ag 1L16 Lecture: Particle filters I Peter Jan van Leeuwen pdf
Las Iguanas, Central Reading Dinner – 19.00
Friday 24 th June 2022
09.15 10.30 Ag 1L16 Lecture: Particle filters II Peter Jan van Leeuwen As above pdf
10.30 11.00 Break
11.00 13.00 PC lab Ag GL20 Practical: Particle filters github
instructions (as above)
13.00 14.00 Lunch break
14.00 15.00 Ag 1L16 Lecture: What to use when Ross Bannister  pdf
15.00 15.15 Certificates and closing words