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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter

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

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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman Filter

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  1. Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter B.-J. ChoiKunsan National University, KoreaGwang-Ho Seo, Yang-Ki Cho, Chang-Sin KimChonnam National University, KoreaSangil KimOregon State University, USAYoung-Ho KimKORDI, Korea

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

  3. Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter • Objectives of current research • setup a Northwest Pacific Ocean Circulation Model • develop an Ensemble Kalman Filter • assimilate Sea Surface Temperature • assimilate temperature profiles • assimilate sea surface height data • 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

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

  5. Nesting of a coastal ocean modeling system 25 km

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

  7. Ocean Circulation Model for the Northwest Pacific Ocean and its Marginal Seas • ROMS (Regional Ocean Modeling System) • 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

  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

  10. MAY FEB MODEL SST MAY FEB SATELLITE SST

  11. NOV AUG MODEL SST NOV AUG SATELLITE SST

  12. Transport through Korea Strait

  13. Transport through Korea Strait

  14. 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

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

  16. Data Assimilation in an Ocean Modeling System 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.

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

  18. Ensemble Kalman Filter (EnKF)

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

  20. Identical Twin Experiment (30 SST observation points) The 30 diamond marks (♦) are the locations for measurement.

  21. Identical Twin Experiment Surface Currents Surface Currents Surface Currents

  22. Identical Twin Experiment RMS error of SST

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

  24. Assimilation of Satellite-observed SST 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

  25. Assimilation of Satellite-observed SST The 50 red dots ( ) are the locations for SST measurement point.

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

  27. (sea surface height)

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

  29. Sea Surface Temperature (day) Sea Surface Height (day)

  30. East Sea (Japan Sea) YS KE ECS

  31. RMSE in Sea Surface Temperature

  32. RMSE in Sea Surface Height

  33. RMSE of SST with respect to in-situ observation

  34. Comparison with observed Temperature (100 m)

  35. Comparison of Temperature Profile

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

  37. 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.

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

  39. RMSE of Sea Surface Height (m)

  40. Comparison of Temperature Profile (May 2004)

  41. 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

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

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