1. 1 Modeling and Forecasting for SCCOOS (Southern California Coastal Ocean Observing System) Yi Chao 1, 2 & Jim McWilliams 2 1 Jet Propulsion Laboratory, California Institute of Technology & 2 University of California, Los Angeles

2. 2 ------OUTLINE------ Implementation Evaluation & Validation Refinement, Improvement, Enhancement Model Derived Products

3. 3 SCCOOS Forecasting System: Real-Time 24/7 since April 2007; 6-hour nowcast; daily 72-hour forecast http://ourocean.jpl.nasa.gov/SCB http://www.sccoos.org/data/roms

4. 4 Forecast skill depends on (1) model (physics), (2) initial condition (observations), (3) ensemble spread (data assimilation)

5. 5 ROMS Model Configurations Dong, McWilliams et al., 2009: Circulation and Multiple-scale Variability in the Southern California Bight, Progress in Oceanography,82,168-190. 1-km Southern California Bight (SCB) 15-km US West Coast (3-km California, real- time 24/7 by Oct. 2010)

6. 6 SCCOOS (routine) Observing System

7. 7 High-Frequency (HF) Radar

8. 8 Satellite Sea Surface Temperature (SST) Microwave: 25-km GOES: 5-km MODIS: 1-km

9. 9 SCCOOS AUV Glider Lines (2)

10. 10 Data Assimilation: Incremental 3DVAR (6-hour window) J = 0.5 (x-x f ) T B -1 (x-x f ) + 0.5 (h x-y) T R -1 (h x-y) Time Aug.1 00Z Aug.1 18Z Aug.1 12Z Aug.1 06Z Initial condition 6-hour forecast Aug.2 00Z x a = x f +  x f xaxa xfxf 3-dimensional variational (3DVAR) method: 3-day forecast y: observation x: model 6-hour assimilation cycle

11. 11 Satellite SST/SSH HF Radar LR-3DVAR Forecast Smoothed Start HR-3DVAR Low-Res Increment End Glider/Argo/Mooring Smoothed Multi-Scale 3DVAR Data Assimilation Two-Stage High-Res Increment t+1 Low-Res. Obs. High-Res. Obs.

12. 12 Low-Res. Obs. High-Res. Obs. Multi-Scale 3DVAR Data Assimilation Two-Stage

13. 13 3DVAR Unique Implementation: Geostrophic & Hydrostatic Balance U/V vs. Streamfunction/Velocity-Potential Geostrophic balance Hydrostatic equation Five Control Variables: Temperature: δT Salinity: δS Non-steric SSH: δX aζ Ageostrophic streamfunction: δX aψ Ageostrophic velocity potential: δX aχ

14. 14 JPL/UCLA Data Assimilation Publications Li, Z., Yi Chao, and J.C. McWilliams, Computation of the Streamfunction and Velocity Potential for Limited and Irregular Domains, Monthly Weather Review, 134, 3384-3394, 2006. Li, Z., Y. Chao, J. C. McWilliams, and K. Ide: A three- dimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments. Journal of Geophysical Research (Oceans), 113, C05002, 2008. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide: A Three- Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090, 2009. Li, Chao, McWilliams, Ide, Multi-scale 3DVAR data assimilation for coastal oceans, Manuscript to be submitted, 2010.

15. 15 Evaluation: Surface Current in 2007 May July Sept. Nov.

16. 16 Evaluation: Glider Profiles

17. 17 Evaluation: Glider Profiles RMS = 0.623 RMS = 0.375

18. 18 Validation: Tides

19. 19 Validation: Mooring (UCLA) Santa Monica Bay Mooring ROMS

20. 20 Validation, Validation, & Validation Mooring, ADCP current data, –Short duration: 2-week ONR RaDyo field experiment in Santa Barbara Channel –Near-shore, shallow waters CalCOFI Ship Survey –Quarterly ???ongoing…community effort???

21. 21 Total currents data assimilation (circle) 1 st Guess (blue) Reanalysis (red) Radial current data assimilation (triangle) New data to be assimilated: Radial HF radar current

22. 22 August 2003 (Black: SIO glider; Red: ROMS) Week 1Week 2 New data to be assimilated: Glider-derived vertically integrated current

23. 23 Curtis Deutsch (UCLA) & Fei Chai (Univ. of Maine) Coupling ROMS Circulation with Biogeochemical Cycle & Ecosystem (NPZ)

24. 24 Initial results are encouraging; Next Steps: Validation; Real-time implementation; Biological data assimilation; HAB prediction

25. 25 Model-Derived Product: Ensemble Mean Forecast and Uncertainty 72-hour forecast 16-member ensemble Mean Error

26. 26 Model-Derived Product: Web-Based Interactive Surface Trajectory Other Applications: Search & rescue, Oil spill response, Water quality, Ecosystem/fishery

27. 27 Contact: Yi.Chao@jpl.nasa.gov; jcm@atmos.ucla.edu JPL ROMS Group: Gene Li, John Farrara, Xin Jin, Peggy Li, Quoc Vu, Adam Wang, Carrie Zhang In collaboration with UCLA ROMS group led by Prof. Jim McWilliams