1. Assimilation of satellite data in
OpenDA
Localization and local analysis
1

2. Outline
• Spurious correlations
• Localization weights
• Subspace problem
• Satellite observations
• Local analysis in OpenDA 3.0
2

3. Spurious correlations
3

4. Spurious correlations
4

5. Spurious correlations
5

6. Localization weights
6

7. Localization weights
7

8. Subspace problem
• The updated state is a linear combination of the ensemble pairs
• Order of ensemble O(10)-O(100)
• Update is in low dimensional subspace of model dimension
8

9. Subspace problem
• Localization (only updating around observations) adds a lot of freedom
• True state can be approximated much better
9

10. Satellite observations
• Very large number of observations O(1000)-O(10000)
• # Ensemble members << #observations
• Size gain matrix #state elements x #observations
• # Ensemle members x #state elements x #observations
• Localization mask!
• Update small domains
• Only near observations
10
😳
😀

11. Satellite observations
11

12. Local analysis in OpenDA 3.0
• Models:
− Localization “distance” (removed from algorithm)
− Definition of sub domains
− Operation on subdomains (get/set/axpy)
− Sort out relevant observations per domain
• Algorithms:
− Removed localization distance
− Local analysis version
12