280353 VU Advanced Data Assimilation (2024S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 05.02.2024 00:00 to Tu 27.02.2024 23:59
- Registration is open from Th 29.02.2024 00:00 to We 06.03.2024 23:59
- Deregistration possible until Su 31.03.2024 23:59
Details
max. 15 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
1. Lecture on March 1st, 2024 at 9 a.m.
Room: 2G542 (UZA II)
There will likely be no lecture or exercise during the EGU (April, 15 - 19)
- Friday 01.03. 09:00 - 11:00 Ort in u:find Details
- Friday 01.03. 11:15 - 12:15 Ort in u:find Details
- Friday 08.03. 09:00 - 11:00 Ort in u:find Details
- Friday 08.03. 11:15 - 12:15 Ort in u:find Details
- Friday 15.03. 09:00 - 11:00 Ort in u:find Details
- Friday 15.03. 11:15 - 12:15 Ort in u:find Details
- Friday 22.03. 09:00 - 11:00 Ort in u:find Details
- Friday 22.03. 11:15 - 12:15 Ort in u:find Details
- Friday 12.04. 09:00 - 11:00 Ort in u:find Details
- Friday 12.04. 11:15 - 12:15 Ort in u:find Details
- Friday 19.04. 09:00 - 11:00 Ort in u:find Details
- Friday 19.04. 11:15 - 12:15 Ort in u:find Details
- Friday 26.04. 09:00 - 11:00 Ort in u:find Details
- Friday 26.04. 11:15 - 12:15 Ort in u:find Details
- Friday 03.05. 09:00 - 11:00 Ort in u:find Details
- Friday 03.05. 11:15 - 12:15 Ort in u:find Details
- Friday 10.05. 09:00 - 11:00 Ort in u:find Details
- Friday 10.05. 11:15 - 12:15 Ort in u:find Details
- Friday 17.05. 09:00 - 11:00 Ort in u:find Details
- Friday 17.05. 11:15 - 12:15 Ort in u:find Details
- Friday 24.05. 09:00 - 11:00 Ort in u:find Details
- Friday 24.05. 11:15 - 12:15 Ort in u:find Details
- Friday 31.05. 09:00 - 11:00 Ort in u:find Details
- Friday 31.05. 11:15 - 12:15 Ort in u:find Details
- Friday 07.06. 09:00 - 11:00 Ort in u:find Details
- Friday 07.06. 11:15 - 12:15 Ort in u:find Details
- Friday 14.06. 09:00 - 11:00 Ort in u:find Details
- Friday 14.06. 11:15 - 12:15 Ort in u:find Details
- Friday 21.06. 09:00 - 11:00 Ort in u:find Details
- Friday 21.06. 11:15 - 12:15 Ort in u:find Details
- Friday 28.06. 09:00 - 11:00 Ort in u:find Details
- Friday 28.06. 11:15 - 12:15 Ort in u:find Details
Information
Aims, contents and method of the course
Assessment and permitted materials
Lectures: Oral exam (date t.b.a.)
Exercises: Presenting results of hands-on exercises
Exercises: Presenting results of hands-on exercises
Minimum requirements and assessment criteria
The weighting of lecture and exercise in final grade:
Lectures: 65%
Exercises: 35%Note 1: > 87,5%
Note 2: 75-87,5%
Note 3: 62,5-75%
Note 4: 50-62,5%
Note 5: < 50%Attendance of a minimum of 80% of exercises is required.
Lectures: 65%
Exercises: 35%Note 1: > 87,5%
Note 2: 75-87,5%
Note 3: 62,5-75%
Note 4: 50-62,5%
Note 5: < 50%Attendance of a minimum of 80% of exercises is required.
Examination topics
The entire content of lectures and exercises.
Reading list
Eugenia Kalnay - Atmospheric Modeling, Data Assimilation and Predictability
Anderson, J., et al. (2009) - The Data Assimilation Research Testbed: A Community Facility
Anderson, J., et al. (2009) - The Data Assimilation Research Testbed: A Community Facility
Association in the course directory
WM-AdvWea
Last modified: We 28.02.2024 15:47
* Ensemble data assimilation
* Covariance modeling and localization in different systems
* Satellite data assimilation
* Hybrid Ensemble-Variational (EnVar)
* Observation impact
* Ensemble sensitivity
* Parameter estimation
* Hands-on modeling with WRF-DART
* Exercises will focus on selected topics>>> Goals:
Students...
* understand fundamental concepts of data assimilation.
* are familiar with important methods (localization, inflation, ...) in ensemble data assimilation.
* can perform data assimilation using a state-of-the-art numerical modeling system.
* can analyze data assimilation output using Python.
* are able to interpret and critically evaluate data assimilation processes.>>> Methods:
* Lectures on data assimilation
* Discussion of state-of-the-art research (via ISDA-Online)
* Conduct, analyze, and discuss numerical experiments.