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

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?

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

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

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

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

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)

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

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

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

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

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