1. OSSEs for Pacific Predictability Josh Hacker, NCAR
Contributors: J. Anderson, R. Atlas, S. Benjamin, D. Emmitt, R. Frehlich, G. Hakim, J. Hansen, T. Hamill, R. Morss, C. Snyder, J. Whitaker

2. Large shops and small shops
Some questions clearly require a (nearly) complete operational data stream and state-of-the-art assimilation system
Some questions can be addressed independently
Is there room for both?

3. Operational questions
Value of existing and proposed observations to analysis and forecast skill
Impact of observations in the context of all other observations
Cost associated with ingestion, QC, and assimilation of an additional observation

4. Operational OSSE themes
Must be carefully designed
Multiple models (a credibility issue)
Total observation error is difficult to estimate
Interpretation can be done collaboratively

5. Example: Observation error
Rawinsonde in center of grid cell
Large variations in sampling error
Dominant component of total observation error in high turbulence regions
Very accurate observations in low turbulence regions
Courtesy R. Frehlich

6. Small shops as an interpreter
Only need to deal with output and know experiment details
Interpretation of large-shop OSSEs
Eliminating complications and using simpler models to aid interpretation

7. Hierarchical OSSEs
Simpler models can be a useful complement to larger, more complex systems
More accessible to smaller shops
If questions are posed carefully, many results can be extrapolated to full systems
Can rule out observations with simpler models (caveats)

8. OSSEs to develop paradigms
Data assimilation methodology
Approaches to understanding model error
Approaches to understanding model phenomenology
A (near) perfect-model OSSE makes these things far more tractable and serve as a test-bed

9. Examples from A Community White Paper
State estimation: adaptive observing strategies for different forecast objectives
Model error: proposing frameworks for quantifying it
Error dynamics: understanding the interaction between observation networks and phenomenological error growth
Observing network design: basic information content of classes of observations in the context of different DA systems

10. State estimation: adaptive observing strategies for different forecast objectives
Rocket Buoy System
COSMIC
Aerosonde

11. Observing network design
sample case: 500 hPa geopotential
Observing network design
5500 m contour is thickened
Black dots show pressure ob locations
Full CDAS
(120,000+ obs)
EnSRF 1895
(214 surface
pressure obs)
RMS = 39.8 m
Optimal
Interpolation 1895
(214 surface
pressure obs)
RMS = 82.4 m
Courtesy Hamill/Whitaker

12. An incomplete laundry list
Projection of observations on gravity or spurious modes
Testing a variety of (new?) metrics
Observations to impact societal benefit
Disparate and similar observing and model scales
Understanding scale interactions in models