1. New Approaches to Data Assimilation
THORPEX Pacific Predictability Experiment workshop 6 June 2005
Greg Hakim1 & Pierre Gauthier2
1University of Washington
2Meteorological Service of Canada
Contributors: Jeff Anderson, John Derber,
Brian Etherton, Geoff DiMego, Josh Hacker, Tom Hamill,
Jim Hansen, Steve Lord, Sharan Majumdar,
Michael Morgan, Rebecca Morss, Carolyn Reynolds, Chris Snyder, & Istvan Szunyogh.

2. Hakim & Gauthier---New Approaches to Data Assimilation
Goals & Background
How can THORPEX accelerate progress in DA?
What DA advances would most impact THORPEX?
Answer depends upon whether THORPEX aims for:
incremental improvements to existing systems OR
forward-looking research to next-generation systems?
Main issues:
model error.
targeted data assimilation.
observing network design.
needs for OSSEs/OSEs and field campaigns.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

3. Research & Operational Communities
Complementary strengths and agendas.
THORPEX provides an opportunity for greater interaction between these two communities:
faster progress
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

4. Strengths / Role / Interests of the Research Community
basic science
new DA techniques.
predictability
DA as a tool for dynamics.
idealized testing & rapid prototyping (e.g. DART).
pseudo-operational DA on small scale.
interest mainly in ensemble filters
3dvar (mainly for testing new ob types)
4dvar too hard for academic community?
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

5. Strengths / Role / Interests of the operational community
controlled, incremental changes to operational systems.
obs gathering, quality control, and error specification.
forward operators.
satellite radiance assimilation.
supercomputing facilities.
OSE/OSSEs with operational systems.
Main interest: 4dvar
minority interest in ensemble filters.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

6. Generic DA Future Issues
multi-scale obs & increments.
convective scale ---> planetary scale.
model error.
observation data deluge.
automatic data selection "crucial" for future.
non-Gaussian errors.
coupled DA; chemistry DA.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

7. Hakim & Gauthier---New Approaches to Data Assimilation
Ensemble Filters

Analyses Forecasts Analyses
model
runs
observation
assimilation
Essential aspect: ensemble estimate of background error covariance matrix
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

8. MSLP ob: 500 hPa Analysis Increment
3DVAR EnKF
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

9. Mesoscale Example: cov(|V|, qrain)
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

10. Hakim & Gauthier---New Approaches to Data Assimilation
4dvar
background
analysis
observations t0 t
Essential aspect: fits observations over a time period that satisfy model dynamics.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

11. Ensemble Filter Issues
model error: background.
sampling error.
balance.
serial obs. processing
some solutions exist.
sequential update problematic for predominantly asynchronous obs.
tighter coupling at expense of modularity?
filter wrapping in e.g. Python.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

12. Hakim & Gauthier---New Approaches to Data Assimilation
4DVAR Issues
model error: background & obs estimates.
adjoint model
linearity (multi-scale).
~fixed background errors (multi-scale).
initial time.
deterministic analysis.
NCEP: cost-effective 4dvar
limited run-time availability.
“situation dependent” background errors.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

13. 4DVAR-Ensemble Filter Fusion
Ensemble background errors in 4dvar:
ETKF + 4dvar update for mean.
4dvar update on members.
EnKF/3DVAR hybrids.
Kalman smoothers.
“Analysis of record”
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

14. Key DA Research & Applications
[Operational systems].
Model error.
Targeted data assimilation.
Dynamics.
Observing network design.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

15. Hakim & Gauthier---New Approaches to Data Assimilation
Model Error
model calibration.
e.g. parameter estimation.
systematic, conditional, development.
new changes conditioned on existing model structure.
use DA to construct frameworks.
e.g. state-dependent model error.
non-Gaussian errors.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

16. Targeted Data Assimilation
metric-dependent filtered obs stream.
not just satellite data thinning!
e.g. ob usage depends on forecast lead time.
e.g. ob usage depends on forecast metric.
required for THORPEX wide-ranging time/space scales?
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

17. Hakim & Gauthier---New Approaches to Data Assimilation
Dynamics
scale interactions.
predictability error growth.
errors conditioned on flow structure.
new tool for dynamics.
e.g. ensemble potential vorticity inversion.
balance equations ensemble inversion recovered divergence
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

18. Observing Network Design
transition the “organically grown” network.
where to move old obs and/or when to use.
e.g. radiosondes.
new fixed optimal obs sites & types.
basic science and practical aspects.
entanglement with DA approach & norms
try many.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation

19. Hakim & Gauthier---New Approaches to Data Assimilation
Summary
THORPEX and DA need each other.
basic science & operational advances.
Ensemble filters, 4dvar, fusion.
Pacific campaign
Model error.
best obs for state-dependent calibration.
Targeted data assimilation.
Oversampling for OSEs.
6 June 2005
Hakim & Gauthier---New Approaches to Data Assimilation