Post on 06-Feb-2016
description
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A Data Assimilation System for Costal Ocean Real-Time Predictions
Zhijin Li and Yi ChaoJet Propulsion Laboratory, California Institute of Technology
James C. McWilliams (UCLA), Kayo Ide (UMD)
ROMS Meeting, April 5-8, 2010, Hawaii
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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
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2003 Autonomous Ocean Sampling Network (AOSN) Experiment
4 http://ourocean.jpl.nasa.gov
Southern California Bight Real-Time System
Data Assimilation
HF Radar Observation
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Prediction of Drifter Trajectories in the Prince William Sound
Oil Spill: 1989 Exxon Tanker Wreck ,Prince William Sound, Alaska
L0 10kmL1 3.6kmL2 1.2km
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Release time: July 25th, 02 GMT, end time: July 28th, 02 GMT.
Ensemble of Co-located ROMS Simulated Trajectories
PWS 2009 Field Experiment
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Data Assimilation and Forecasting Cycle
3-day forecast
Aug.100Z
Time
Aug.118Z
Aug.112Z
Aug.106Z
Initialcondition
6-hour forecast
Aug.200Z
xa
xf
6-hour assimilation cycle
xxx fa
Time scales comparable with those of the atmosphere
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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)
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21min 11
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yHxRyHxxxBxxJ TfTf
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Weak Geostrophic Constraint:Decomposition of Balanced and Unbalanced Components
TSfTS
aaTSfuv
aTSf
TS
uv
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STvu
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aaTSuv xxx TS
Guv xx
aTS xxx
TSS xx
Geostrophic balance
Geostrophic sea surface level
ax Ageostrophic streamfunction and velocity potential
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Kronecker Product Formulation of 3D Error Correlations
TTT
GGGG
GGGGC
CCC
CCC
111
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Inhomogeneous and anisotropic 3D correlations
Cross-shore and vertical section salinity correlation
Non-steric SSH correlations
(Li et al., 2008, JGR)
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Assimilation of Multi-Satellite SSTs and SSHs
Infrared and Microwave SST Sea Surface Heights
JASON-1
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Assimilation of Real-Time High Frequency Radar Velocities
Short distance: 100km, res of 1km, 5 MHzLong distance:
200km, res of 5km, 25 MHz
http://www.cocmp.org/
2008-12-08
http://www.sccoos.org/
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Comparison of Glider-Derived Currents (vertically integrated current)
Black: SIO glider; Red: ROMSSALT(PSU)
Performance of ROMS3DVAR: AOSN-II, August 2003
(Chao et al., 2009, DSR)
TEMP(C)
Glider temperature/salinity profiles
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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
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Multi-Scale Data Assimilation: Concept
SL
SL
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21min
)()(21)()(
21min
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11
SSSST
SSSfSSS
TfSSx
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TfLLx
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yxHRyxHxxBxxJ
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L
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SL yyyHxy
Background
Observation
Multi-scale DA
(Boer, 1983, MWR)
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Multi-Scale Data Assimilation: Scheme
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safLa
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aL
fL
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Large Scale
aL
fafL xxx Small Scale
Sparse Obs
High Resolution Obs
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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)
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Root-Mean Squared Errors (RMSEs) at 30m
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Root-Mean Squared Errors (RMSEs)at 50m
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3DVAR
MS3DVAR
NO-DA
RMSEs
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3DVAR
MS3DVAR
NO-DA
RMSEs
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SCB Operational System: 3DVAR vs MS3DVAR
3DVAR
MS3DVAR
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HF Radar and Data Assimilation Analysis Velocities
Standard 3DVAR MS-3DVAR
Correlation RMSE Correlation RMSE
U 0.62 0.13m/s 0.75 0.11m/s
V 0.68 0.11m/s 0.82 0.08m/s
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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
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Backup
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MS3DVAR Work Flow
LS-3DVAR -SS-3DVAR
Increment
Obs (Glider, Satellite, HF radar, etc)
Large Scale (LS) Small Scale (SS)Forecast fx
Large Scale fLx
aS
afL
a xxx aS
fa xxx
aLx
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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
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RMSE, ETKF RMSE, MS-3DVAR
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(Lorenc 2003)
A Hybrid Ensemble MS-3DVAR
Applied to the small-scale components