1. Application of Satellite Data in the Data Assimilation Experiments off Oregon Peng Yu in collaboration with Alexander Kurapov, Gary Egbert, John S. Allen, P. Michael Kosro College of Oceanic and Atmospheric Sciences Oregon State University Supported by ONR

2. Complicated dynamics on the shelf in the coastal transition zone (CTZ): -Strong upwelling season -Modeling sensitive to many factors (model resolution, horizontal eddy viscosity, bathymetry, boundary conditions, forcing) -Use data assimilation to improve prediction, forecasting, and scientific understanding of shelf and CTZ flows 6-km, visc=10 m 2 /s Model details: Regional Ocean Modeling System (ROMS) - 6km horizontal resolution and 15 vertical level - NOOA -NAM wind & heat flux - NCOM-CCS boundary conditions (Shulman et al., NRL) (shown: SST Jul. 20, 2008)

3. Available observations - HF radar surface velocities (daily maps, provided by PM Kosro, OSU) Combination of several standard and long- range radar provides time-series info about shelf, slope and CTZ flows - SSH along track altimetry (Jason, Envisat) - satellite SST(D. Folley, NOAA CoastWatch) - gliders (T and S sections, once every 3 days, J Barth and R. K. Shearman, OSU) – 3D information Bathymetric contours: 1000 and 200 m) How does each of these data types contribute to data assimilation?

4. Variational (representer-based) data assimilation in a series of 3-day time windows, June 1– July 30, 2008: In each window, (1)correct initial conditions (use tangent linear &adjoint codes AVRORA, developed at OSU, Kurapov et al., Dyn. Atm. Oceans, 2009) (2)run the nonlinear ROMS for 6 days (analysis + forecast) assim (TL&ADJ AVRORA) forecast (NL ROMS) prior analysis forecast

5. Initial Condition Error Covariance (Dynamically balanced): multivariate u, v, SSH, T, S – geostrophy, thermal-wind Implement the balance operator A (Weaver et al. 2005): univariate covariance for mutually uncorrelated fields s Adj solution at ini time A: Uncorrelated fields: error in T and depth-integrated transport (uH, vH)  S (using constant T-S relationships) horizontal density gradients vertical shear in u, v (thermal wind balance) SSH (2 nd order ellipitic eqn.) u, v (surface current in geostrophic balance with SSH)

6. Observed and prior model SST and surface currents

7. Initial test with one 3-day assimilation window (balanced covariances better in SST forecast) RMSE Correlation Surface VelocitySST (not assimilated) AnalysisForecast AnalysisForecast SST data provided by D. Foley, NOAA CoastWatch

8. Same experiment: extend the forecast to 15days SST (not assimilated)Surface Velocity RMSE Correlation Analysis Forecast AnalysisForecast SST RMSE and correlation are improved for 15 days, after the 1 st assimilation cycle Balanced better

9. 60-day assimilation (June 1-July 30, 2008; 20 assimilation windows): Both Surface velocity and SST are improved RMSE Correlation Surface VelocitySST (not assimilated)

10. Model data comparison: Surface currents (assimilated) and SST (not assimilated) Assimilation of HF radar surface currents improves the geometry of the upwelling SST front

11. Verification SSH, prior, HF radar velocity assimilation Assimilation of HFR data improves SSH, compared to along-track altimetry (not assimilated) in the area covered by the HF Radar Data coverage

12. Cont. (another pass) Data coverage Verification SSH, prior, HF radar velocity assimilation

13. ObservationPrior Analysis (balanced) Analysis (Imbalanced) Comparison against Hydrographic data -The assimilation of the HF Radar surface currents data improves the density structure in the hydrographic sections south of Cape Blanco in the separation zone Data provided by Bill Peterson and Jay Peterson) NH CC RR

14. Real-Time DA and Forecast Experiment Real Time Data: GOES satellite hourly SST composite and surface current maps from HF Radar DA 6km combined with a 3km forecast model Prior and Forecast solutions are different

15. GOES hourly SST composites (2010-06-01)

16. GOES hourly SST composites (2010-06-07)

17. GOES hourly SST composites (2010-06-12)

18. SST RMSE against blended SST (D. Foley)

19. SST correlation against blended SST (D. Foley)

20. Surface current RMSE against HF Radar maps

21. Surface current correlation against HF Radar maps

22. SST daily average

27. Summary The representer-based data assimilation system improves the forecast of the model variables (e.g. SST, surface currents, SSH, density) The assimilation of a unique set of long-, standard-range HF radar observations has a positive impact on the area of the shelf, slope and part of the open ocean The inclusion of the SST observations into the DA system extend the DA impact area to the whole domain

28. Future work More careful quality control about the observations Include more SST observations from other satellites, e.g., AMSR-E, to get a better coverage Test combination of different types of data, e.g., satellite along-track altemetry SSH, T, S from sea gliders