Universität Wien

280352 VU Data Assimilation and Ensemble Methods (2025W)

6.00 ECTS (4.00 SWS), SPL 28 - Erdwissenschaften, Meteorologie-Geophysik und Astronomie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Room 2G542, the door to the G-wing opens with the button "Türöffner" to the right of the door

  • Tuesday 07.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 07.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 07.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 14.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 14.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 14.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 21.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 21.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 21.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 28.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 28.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 28.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 04.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 04.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 04.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 11.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 11.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 11.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 18.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 18.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 18.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 25.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 25.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 25.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 02.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 02.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 02.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 09.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 09.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 09.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 16.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 16.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 16.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 13.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 13.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 13.01. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 20.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 20.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 20.01. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 27.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 27.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 27.01. 14:10 - 15:10 Ort in u:find Details

Information

Aims, contents and method of the course

The students will familiarize themselves with common data-assimilation and ensemble-prediction methods. They shall understand the underlying theory as well as the application. Topics that the students will develop a deeper understanding of include observation operators, Ensemble-Kalman-Filters, adjoint models, 3D-VAR & 4D-VAR, and ensemble perturbations.
The exercises will focus on applying the methods introduced in the lecture using simple numerical examples with Python jupyter-notebooks.

Assessment and permitted materials

There will be two graded oral exams. The first one will occur in November and the second one at the end of the semster. Furthermore, students have to complete assigned tasks at home and participate in the exercises. These assigned tasks will also be graded.

No auxilliary devices allowed.

Minimum requirements and assessment criteria

Die final grade is a weighted mean of the two oral exams and the graded homework assignments. Each of the oral exams counts for 35%, and the homework assignments for 30% of the final grade. The students need to attend at least 80% of the exercises.

Grade 5: < 50%
Grade 4: 50 - 62.49%;
Grade 3: 62.5 - 74.99%;
Grade 2: 75 - 87.49%;
Grade 1: 87.5 - 100%

Examination topics

Entire lecture content (slides uploaded in Moodle)

Reading list

Unfortunately, there are no suitable textbooks.

Association in the course directory

PM-DA-EPS

Last modified: We 24.09.2025 10:07