Universität Wien
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280352 VU Data Assimilation and Ensemble Methods (2024W)

6.00 ECTS (4.00 SWS), SPL 28 - Erdwissenschaften, Meteorologie - Geophysik und
Continuous assessment of course work
Tu 01.10. 10:30-12:00 Ort in u:find Details

Registration/Deregistration

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Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Room 2G542

  • Tuesday 01.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 01.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 08.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 08.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 08.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 15.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 15.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 15.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 22.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 22.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 22.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 29.10. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 29.10. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 29.10. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 05.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 05.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 05.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 12.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 12.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 12.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 19.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 19.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 19.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 26.11. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 26.11. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 26.11. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 03.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 03.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 03.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 10.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 10.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 10.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 17.12. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 17.12. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 17.12. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 07.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 07.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 07.01. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 14.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 14.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 14.01. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 21.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 21.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 21.01. 14:10 - 15:10 Ort in u:find Details
  • Tuesday 28.01. 10:30 - 12:00 Ort in u:find Details
  • Tuesday 28.01. 12:30 - 14:00 Ort in u:find Details
  • Tuesday 28.01. 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. Python and jupyter-notebooks.

Assessment and permitted materials

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

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: 50-62,5%;
Grade 3: 62,5-75%;
Grade 2: 75-87,5%;
Grade 1: > 87,5%

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: Fr 27.09.2024 07:06