1. Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter B.-J. Choi Kunsan National University, Korea Gwang-Ho Seo, Yang-Ki Cho, Chang-Sin Kim Chonnam National University, Korea Sangil Kim Oregon State University, USA Young-Ho Kim KORDI, Korea

2. 1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation of Temperature Profiles Summary Contents Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

3. Objectives of current research (1)setup a Northwest Pacific Ocean Circulation Model (2)develop an Ensemble Kalman Filter (3)assimilate Sea Surface Temperature (4)assimilate temperature profiles (5)assimilate sea surface height data (6)evaluate assimilative model output Long Term Goal Developing a Regional Ocean Prediction System for the Northwest Pacific Ocean and its marginal seas Providing initial condition and open boundary data for high resolution (10km, 3km, 1km) coastal ocean models Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

4. Nesting of a coastal ocean modeling system Nesting 25 km 10 km 1 km 3 km

5. Nesting of a coastal ocean modeling system 25 km

6. 1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation of Temperature Profiles Summary Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

7. ROMS (Regional Ocean Modeling System) 1.Grid size: - Horizontal: ¼ degree - Vertical: 20 layers 2. Topography - ETOPO5 data 3. Initial - WOA2001 4. Surface forcing - ECMWF daily wind - Heat flux : Bulk Flux parameterization 5. River: Changjiang, Hanghe Yellow rivers 6. Tidal forcing along the boundary 7. open boundary data: ECCO Ocean Circulation Model for the Northwest Pacific Ocean and its Marginal Seas

8. Ocean Circulation Model for the Northwest Pacific Ocean and its Marginal Seas Wind Forcing

9. Ocean Circulation Model for the Northwest Pacific Ocean and its Marginal Seas

11. MODEL SST SATELLITE SST FEB MAY

12. AUG NOV MODEL SST SATELLITE SST AUG NOV

14. Transport through Korea Strait

16. Northwest Pacific Ocean Model has problems to be resolved: 1. Overshooting of western boundary currents 2. Temperature bias – model temperature is warmer than observed temperature

17. 1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation of Temperature Profiles Summary Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

18. Ensemble Kalman Filter (EnKF) The technical methods are based on the original algorithm of Evensen (1994) and the modified algorithm of Burgers et al. (1996). It is relatively easy to implement the algorithm, the EnKF, to a sophisticated nonlinear model (e.g. ROMS, POM, ECOM, FVCOM, HYCOM) since the data assimilation algorithm is independent of the forecast model. Data Assimilation in an Ocean Modeling System

19. Member16 Member2 Member1 Ensemble Kalman Filtering (a stochastic process) Data Assimilation in an Ocean Modeling System

20. Ensemble Kalman Filter (EnKF)

21. Number of Ensemble: 16 member 2003/01/012003/12/31 Spin up time assimilation True run Control run Ensemble run start Identical Twin Experiment

22. The 30 diamond marks ( ♦ ) are the locations for measurement. (30 SST observation points)

23. Identical Twin Experiment Surface Currents

24. Identical Twin Experiment RMS error of SST

25. 1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation of Temperature Profiles Summary Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

26. Modeling Domain: Northwest Pacific Ocean and its Marginal Seas Duration: November 2003 to June 2004 SST data: NASA composite SST (AVHRR, AMSR, buoy ) Assimilation method: Ensemble Kalman Filter Assimilation interval: every 7 day Ensemble number: 16 Number of observation points: 50 Assimilation of Satellite-observed SST

27. The 50 red dots ( ) are the locations for SST measurement point.

28. Initialization of 16 ensemble members using EOF analysis of model output E (x) is ensemble initial, state vector on November 1, 2003, ξ a is normal random number, r eigenvalues, e eigenvectors, and p mode number. (sea surface height)

30. Member16 Member2 Member1 Ensemble Kalman Filtering (a stochastic process) Assimilation of Satellite-observed SST

33. Sea Surface Temperature Sea Surface Height (day)

34. KE East Sea (Japan Sea) YS ECS

35. RMSE in Sea Surface Temperature

36. RMSE in Sea Surface Height

37. FebruaryAprilJune Assi.ControlAssi.ControlAssi.Control YELLOW SEA1.3931.5071.4121.5472.2892.423 EAST SEA0.9531.4471.5471.8651.9022.274 RMSE of SST with respect to in-situ observation

38. Comparison with observed Temperature (100 m)

39. Comparison of Temperature Profile

40. 1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter 3. Assimilation of SST 4. Assimilation of Temperature Profiles Summary Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter

41. Subsurface temperature data from CDT, XBT, and ARGO floats.  we assimilated temperature data at 5, 10, 30, 50, 100, 200, 500 m depths every 7 day. The observation data within 7 days of time window are sampled on the day of assimilation.

42. t = 7 dayt = 14 day t = 21 day t = 28 day t = 35 day t = 42 day t = 49 day t = 56 day t = 63 day

47. RMSE(meter)ControlSST Assi.SST & Hydro Assi. 2004/5/270.2220.1870.181 RMSE of Sea Surface Height (m)

48. Comparison of Temperature Profile (May 2004)

49. Summary Setup a Northwest Pacific Ocean Circulation Model Developed an Ensemble Kalman Filter Performed an identical twin experiment to evaluate the performance of Ensemble Kalman Filter Assimilation of SST Work in Progress Assimilation of Subsurface Temperature Data from CDT, XBT and ARGO Floats Future Plan Setup a higher resolution model and assimilate Sea Surface Height in order to resolve eddies and current meandering Provide initial condition and open boundary data for (3km and/or 1km resolution) coastal ocean models

50. Thank you ! Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter