Presentation by Nils van Velzen (VORtech) at the OpenDA User Day, during Delft Software Days 2016. Tuesday 1 November 2016, Delft.

1. Assimilation of satellite data in
OpenDA
Localization and local analysis
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2. Outline
• Spurious correlations
• Localization weights
• Subspace problem
• Satellite observations
• Local analysis in OpenDA 3.0
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3. Spurious correlations
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4. Spurious correlations
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5. Spurious correlations
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6. Localization weights
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7. Localization weights
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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
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9. Subspace problem
• Localization (only updating around observations) adds a lot of freedom
• True state can be approximated much better
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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
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😳
😀

11. Satellite observations
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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
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