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A Data Assimilation System for Costal Ocean Real-Time Predictions

A Data Assimilation System for Costal Ocean Real-Time Predictions. Zhijin Li and Yi Chao Jet Propulsion Laboratory, California Institute of Technology James C. McWilliams (UCLA), Kayo Ide (UMD). ROMS Meeting , April 5-8, 2010, Hawaii. Outline.

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A Data Assimilation System for Costal Ocean Real-Time Predictions

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  1. A Data Assimilation System for Costal Ocean Real-Time Predictions Zhijin Li and Yi Chao Jet Propulsion Laboratory, California Institute of Technology James C. McWilliams (UCLA), Kayo Ide (UMD) ROMS Meeting, April 5-8, 2010, Hawaii

  2. Outline • Developed costal ocean data assimilation and forecasting systems • Recap on the three-dimensional variational data assimilation • A multi-scale three-dimensional variational data assimilation • Summary

  3. 2003 Autonomous Ocean Sampling Network (AOSN) Experiment

  4. Southern California Bight Real-Time System HF Radar Observation Data Assimilation http://ourocean.jpl.nasa.gov

  5. Prediction of Drifter Trajectories in the Prince William Sound L0 10km L1 3.6km L2 1.2km Oil Spill: 1989 Exxon Tanker Wreck ,Prince William Sound, Alaska

  6. Ensemble of Co-located ROMS Simulated Trajectories PWS 2009 Field Experiment

  7. 3-day forecast 6-hour forecast xf 6-hour assimilation cycle xa Initial condition Time Aug.2 00Z Aug.1 18Z Aug.1 06Z Aug.1 12Z Aug.1 00Z Data Assimilation and Forecasting Cycle Time scales comparable with those of the atmosphere

  8. A There-Dimensional Variational Data Assimilation (3DVAR) • Real-time capability • Implementation with sophisticated and high resolution model configurations • Flexibility to assimilate various observation simultaneously • Development for more advanced scheme (Li et al., 2006, MWR; Li et al., 2008, JGR, Li et al., 2008, JAOT)

  9. Geostrophic balance Geostrophic sea surface level Ageostrophic streamfunction and velocity potential Weak Geostrophic Constraint:Decomposition of Balanced and Unbalanced Components

  10. Kronecker Product Formulation of 3D Error Correlations

  11. Inhomogeneous and anisotropic 3D correlations Non-steric SSH correlations (Li et al., 2008, JGR) Cross-shore and vertical section salinity correlation

  12. JASON-1 Assimilation of Multi-Satellite SSTs and SSHs Sea Surface Heights Infrared and Microwave SST

  13. Assimilation of Real-Time High Frequency Radar Velocities 2008-12-08 Short distance: 100km, res of 1km, 5 MHz Long distance: 200km, res of 5km, 25 MHz http://www.sccoos.org/ http://www.cocmp.org/

  14. Performance of ROMS3DVAR: AOSN-II, August 2003 Comparison of Glider-Derived Currents (vertically integrated current) Black: SIO glider; Red: ROMS SALT(PSU) TEMP(C) Glider temperature/salinity profiles (Chao et al., 2009, DSR)

  15. Southern California Coastal Ocean Observing System (SCCOOS) SIO Glider Tracks Motivation: assimilating sparse vertical profiles along with high resolution observations for a very high resolution model

  16. Background Observation Multi-scale DA Multi-Scale Data Assimilation: Concept (Boer, 1983, MWR)

  17. Large Scale Small Scale Multi-Scale Data Assimilation: Scheme Sparse Obs High Resolution Obs

  18. Twin Experiments: Observations • Model resolution of 1km • SSTs and surface velocities at 2km by 2km • T/S profiles • at 10km by 60km (ideal) • at 10km by 180km (real)

  19. Root-Mean Squared Errors (RMSEs) at 30m

  20. Root-Mean Squared Errors (RMSEs)at 50m

  21. RMSEs NO-DA 3DVAR MS3DVAR

  22. RMSEs NO-DA 3DVAR MS3DVAR

  23. SCB Operational System: 3DVAR vs MS3DVAR 3DVAR MS3DVAR

  24. HF Radar and Data Assimilation Analysis Velocities

  25. Summary • A 3DVAR system has been developed with unique formulations for coastal oceans. • The MS3DVAR system has been demonstrated significantly better skill and computational efficiency, and it has been implemented in operational applications. • For more information on real-time data assimilation and forecasting systems: http://ourocean.jpl.nasa.gov

  26. Backup

  27. MS3DVAR Work Flow Obs (Glider, Satellite, HF radar, etc) Large Scale (LS) Small Scale (SS) Forecast LS-3DVAR SS-3DVAR - Large Scale Increment

  28. ETKF vs MS-3DVAR in Twin experiments • Observations: HF radar velocities and SSTs, along with Sparse T/S profiles • ETKF continuously reduces RMSEs because of the predicted error covariance, while MS-3DVAR more effectively fit to high resolutions observations at the early stage RMSE, MS-3DVAR RMSE, ETKF

  29. A Hybrid Ensemble MS-3DVAR (Lorenc 2003) Applied to the small-scale components

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