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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 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 • 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
Southern California Bight Real-Time System HF Radar Observation Data Assimilation http://ourocean.jpl.nasa.gov
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
Ensemble of Co-located ROMS Simulated Trajectories PWS 2009 Field Experiment
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
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)
Geostrophic balance Geostrophic sea surface level Ageostrophic streamfunction and velocity potential Weak Geostrophic Constraint:Decomposition of Balanced and Unbalanced Components
Inhomogeneous and anisotropic 3D correlations Non-steric SSH correlations (Li et al., 2008, JGR) Cross-shore and vertical section salinity correlation
JASON-1 Assimilation of Multi-Satellite SSTs and SSHs Sea Surface Heights Infrared and Microwave SST
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/
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)
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
Background Observation Multi-scale DA Multi-Scale Data Assimilation: Concept (Boer, 1983, MWR)
Large Scale Small Scale Multi-Scale Data Assimilation: Scheme Sparse Obs High Resolution Obs
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)
RMSEs NO-DA 3DVAR MS3DVAR
RMSEs NO-DA 3DVAR MS3DVAR
SCB Operational System: 3DVAR vs MS3DVAR 3DVAR MS3DVAR
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
MS3DVAR Work Flow Obs (Glider, Satellite, HF radar, etc) Large Scale (LS) Small Scale (SS) Forecast LS-3DVAR SS-3DVAR - Large Scale Increment
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
A Hybrid Ensemble MS-3DVAR (Lorenc 2003) Applied to the small-scale components