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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.

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Presentation on theme: "1 A Data Assimilation System for Costal Ocean Real-Time Predictions Zhijin Li and Yi Chao Jet Propulsion Laboratory, California Institute of Technology."— Presentation transcript:

1 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 2 Outline 1.Developed costal ocean data assimilation and forecasting systems 2.Recap on the three-dimensional variational data assimilation 3.A multi-scale three-dimensional variational data assimilation 4.Summary

3 Autonomous Ocean Sampling Network (AOSN) Experiment

4 4 Southern California Bight Real-Time System Data Assimilation HF Radar Observation

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

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

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

8 8 A There-Dimensional Variational Data Assimilation (3DVAR) 1.Real-time capability 2.Implementation with sophisticated and high resolution model configurations 3.Flexibility to assimilate various observation simultaneously 4.Development for more advanced scheme (Li et al., 2006, MWR; Li et al., 2008, JGR, Li et al., 2008, JAOT)

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

10 10 Kronecker Product Formulation of 3D Error Correlations

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

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

13 13 Assimilation of Real-Time High Frequency Radar Velocities Short distance: 100km, res of 1km, 5 MHz Long distance: 200km, res of 5km, 25 MHz /

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

15 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 16 Multi-Scale Data Assimilation: Concept Background Observation Multi-scale DA (Boer, 1983, MWR)

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

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

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

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

21 21 3DVAR MS3DVAR NO-DA RMSEs

22 22 3DVAR MS3DVAR NO-DA RMSEs

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

24 24 HF Radar and Data Assimilation Analysis Velocities Standard 3DVAR MS-3DVAR CorrelationRMSECorrelationRMSE U m/s m/s V m/s m/s

25 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:

26 26 Backup

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

28 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 28 RMSE, ETKF RMSE, MS-3DVAR

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


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