1 a three-dimensional variational data assimilation in support of coastal ocean observing systems...
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A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems
Zhijin Li and Yi ChaoJet Propulsion Laboratory
Jim McWilliams and Kayo Ide
UCLA
ROMS User Workshop, October 2, 2007, Los Angeles
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Integrated Ocean Observing System (IOOS):Modeling, data assimilation, forecasting and adaptive sampling
Theoretical Understanding &
NumericalModels
Products
Users:Managers
Education & Outreach
Observations(satellite, in situ)
Feedback & Adaptive Sampling
Information
Data Assimilation
Observing System Design
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Outline
1. Real-time Regional Ocean Modeling System (ROMS)
2. Three-dimensional variational data assimilation
3. Assimilated observations
4. Evaluation of analyses and forecasts
5. Observing system experiments (OSE)
6. Relocatability
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Coupled with Tides
Sea Surface M2 Tidal Currents
ROMS Simulation HF Radar Obs
RMS Error of SSHs
Tide Gauge
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Regional Ocean Modeling System
(ROMS): From Global to Regional/Coastal
12-km
Multi-scale (or “nested”) ROMS modeling
approach is developed in order to simulate the 3D ocean at the spatial scale (e.g., 1.5-km) measured by in situ and remote
sensors
1.5-km5-km15-kmModeling Approach
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Data Assimilation
Analysis
Forecast
Processing
Observations
When the numerical model is so good as its predictionis superior to the climatological (almanac) forecast
Model
Data Assimilation
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ROMS Analysis and Forecast Cycle:Incremental 3DVAR
Aug.100Z
Time
Aug.118Z
Aug.112Z
Aug.106Z
Initialcondition
6-hour forecast
Aug.200Z
xa
xf
3-day forecast
y: observationx: model
6-hour assimilation cycle
)()(2
1)()(
2
1min 11 yHxRyHxxxBxxJ TfTf
x
xxx fa
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Why a There-Dimensional Variational Data Assimilation
• Real-time capability• Implementation with sophisticated and high
resolution model configurations• Flexibility to assimilate various observation
simultaneously• Development for more advanced scheme
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3DVAR: Weak Geostrophic Constraint and Hydrostatic Balance
TSfTS
aaTSfuv
aTSf
TS
uv
xx
xxx
xxx
x
x
x
S
T
v
u
x
aaTSuv xxx TS
Guv xx
aTS xxx
TSS xx
Geostrophic balance
Vertical integral of the hydrostatic equation
ax ageostrophic streamfunction and velocity potential
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Inhomogeneous and anisotropic 3D Global Error Covariance
Cross-shore and vertical section salinity correlation
SSH correlations
Kronecker Product
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Assimilated observations:Satellite infrared SSTs
NOAA GOES
NOAA AVHRR
Infrared, High resolutionCloud contamination
Microwave, Low resolution (25km)No cloud contamination
NASA AquaAMSR-E
NASA TRIMMTMI
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Assimilated observations:Satellite SSHs along track
JASON-1
Resolution: 120km cross track, 6km along track
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Integrated Ocean Observing Systems
0
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900
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Year Day
Nu
mb
er o
f C
asts
/Day
<55
<110
<220
<440
<1100
T/S profiles from gliders Ship CTD profiles Aircraft SSTs AUV sections
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Assimilated Current Observations
Acoustic Doppler Current Profiler (ADCP)
BottomShipboard Buoy
High Frequency Radar Mapped 2D surface current
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3DVAR with First Guess at Appropriate Time (FGAT)
fttt
tt
ttt
tftt
Ttt
ttt
tfttt
Tt
xxx
yHxxHRyHxxHxBxJ
000
0
0
0
0
0
000
2
1
2
1
12
1
2
1
1
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3.5DVar
If ,0 tt xx FDAT 3DVar is equivalent to 4DVar
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ROMS Performance Against Assimilated Data
August 2006 Mean
Temperature (C)
Salinity (PSU)
All Gliders Mean Diff RMS Diff
-0.3 0.3 0.0 0.75
-0.1 0.1 0.0 0.20
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Comparison of Glider-Derived Currents (vertically integrated current)
AOSN-II, August 2003 Black: SIO glider; Red: ROMS
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Forecast CorrelationPredictability during AOSN-II
Note: because gliders are moving, one cannot estimate the persistence
RMSE
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Observing System Experiment (OSE)
– Typically aimed at assessing the impact of a given existing data type on a system
– Using existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system
– Assessing the impact
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Observing System Experiment (OSE):Glider Data Denial Experiment
Temperature Salinity
1st week 2nd week
CalPoly SIO WHOI
w/o CalPoly glider
with CalPoly glider
RMS Error
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HF radar
ROMS without HF radar data assimilation
ROMS with HF radar data assimilation
Impact of HF Radar
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Southern California Coastal Ocean Observing System (SCCOOS)
http://ourocean.jpl.nasa.gov/SCB
Southern California Bight
US WEST COAST
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Real-Time SCCOOS Data Assimilation and Forecasting System
http://ourocean.jpl.nasa.gov/SCB
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Evaluation with HF radar velocities
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Toward a Relocatable ROMS Forecasting System:Demonstration for Prince William Sound, Alaska
9-km
1-km3-km