Assimilation of Sea Surface Temperature into a Northwest
Pacific Ocean Model using an Ensemble Kalman Filter B.-J. Choi
Kunsan National University, Korea Gwang-Ho Seo, Yang-Ki Cho,
Chang-Sin Kim Chonnam National University, Korea Sangil Kim Oregon
State University, USA Young-Ho Kim KORDI, Korea
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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman
Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation
of Temperature Profiles Summary Contents Assimilation of Sea
Surface Temperature into a Northwest Pacific Ocean Model using an
Ensemble Kalman Filter
Slide 3
Objectives of current research (1)setup a Northwest Pacific
Ocean Circulation Model (2)develop an Ensemble Kalman Filter
(3)assimilate Sea Surface Temperature (4)assimilate temperature
profiles (5)assimilate sea surface height data (6)evaluate
assimilative model output Long Term Goal Developing a Regional
Ocean Prediction System for the Northwest Pacific Ocean and its
marginal seas Providing initial condition and open boundary data
for high resolution (10km, 3km, 1km) coastal ocean models
Assimilation of Sea Surface Temperature into a Northwest Pacific
Ocean Model using an Ensemble Kalman Filter
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Nesting of a coastal ocean modeling system Nesting 25 km 10 km
1 km 3 km
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Nesting of a coastal ocean modeling system 25 km
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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman
Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation
of Temperature Profiles Summary Assimilation of Sea Surface
Temperature into a Northwest Pacific Ocean Model using an Ensemble
Kalman Filter
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ROMS (Regional Ocean Modeling System) 1.Grid size: -
Horizontal: degree - Vertical: 20 layers 2. Topography - ETOPO5
data 3. Initial - WOA2001 4. Surface forcing - ECMWF daily wind -
Heat flux : Bulk Flux parameterization 5. River: Changjiang, Hanghe
Yellow rivers 6. Tidal forcing along the boundary 7. open boundary
data: ECCO Ocean Circulation Model for the Northwest Pacific Ocean
and its Marginal Seas
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Ocean Circulation Model for the Northwest Pacific Ocean and its
Marginal Seas Wind Forcing
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Ocean Circulation Model for the Northwest Pacific Ocean and its
Marginal Seas
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MODEL SST SATELLITE SST FEB MAY
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AUG NOV MODEL SST SATELLITE SST AUG NOV
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Transport through Korea Strait
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Northwest Pacific Ocean Model has problems to be resolved: 1.
Overshooting of western boundary currents 2. Temperature bias model
temperature is warmer than observed temperature
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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman
Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation
of Temperature Profiles Summary Assimilation of Sea Surface
Temperature into a Northwest Pacific Ocean Model using an Ensemble
Kalman Filter
Slide 18
Ensemble Kalman Filter (EnKF) The technical methods are based
on the original algorithm of Evensen (1994) and the modified
algorithm of Burgers et al. (1996). It is relatively easy to
implement the algorithm, the EnKF, to a sophisticated nonlinear
model (e.g. ROMS, POM, ECOM, FVCOM, HYCOM) since the data
assimilation algorithm is independent of the forecast model. Data
Assimilation in an Ocean Modeling System
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Member16 Member2 Member1 Ensemble Kalman Filtering (a
stochastic process) Data Assimilation in an Ocean Modeling
System
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Ensemble Kalman Filter (EnKF)
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Number of Ensemble: 16 member 2003/01/012003/12/31 Spin up time
assimilation True run Control run Ensemble run start Identical Twin
Experiment
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The 30 diamond marks ( ) are the locations for measurement. (30
SST observation points)
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Identical Twin Experiment Surface Currents
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Identical Twin Experiment RMS error of SST
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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman
Filter 3. Assimilation of Sea Surface Temperature 4. Assimilation
of Temperature Profiles Summary Assimilation of Sea Surface
Temperature into a Northwest Pacific Ocean Model using an Ensemble
Kalman Filter
Slide 26
Modeling Domain: Northwest Pacific Ocean and its Marginal Seas
Duration: November 2003 to June 2004 SST data: NASA composite SST
(AVHRR, AMSR, buoy ) Assimilation method: Ensemble Kalman Filter
Assimilation interval: every 7 day Ensemble number: 16 Number of
observation points: 50 Assimilation of Satellite-observed SST
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The 50 red dots ( ) are the locations for SST measurement
point.
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Initialization of 16 ensemble members using EOF analysis of
model output E (x) is ensemble initial, state vector on November 1,
2003, a is normal random number, r eigenvalues, e eigenvectors, and
p mode number. (sea surface height)
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Member16 Member2 Member1 Ensemble Kalman Filtering (a
stochastic process) Assimilation of Satellite-observed SST
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Sea Surface Temperature Sea Surface Height (day)
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KE East Sea (Japan Sea) YS ECS
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RMSE in Sea Surface Temperature
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RMSE in Sea Surface Height
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FebruaryAprilJune Assi.ControlAssi.ControlAssi.Control YELLOW
SEA1.3931.5071.4121.5472.2892.423 EAST
SEA0.9531.4471.5471.8651.9022.274 RMSE of SST with respect to
in-situ observation
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Comparison with observed Temperature (100 m)
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Comparison of Temperature Profile
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1. Northwest Pacific Ocean Model with ROMS 2. Ensemble Kalman
Filter 3. Assimilation of SST 4. Assimilation of Temperature
Profiles Summary Assimilation of Sea Surface Temperature into a
Northwest Pacific Ocean Model using an Ensemble Kalman Filter
Slide 41
Subsurface temperature data from CDT, XBT, and ARGO floats. we
assimilated temperature data at 5, 10, 30, 50, 100, 200, 500 m
depths every 7 day. The observation data within 7 days of time
window are sampled on the day of assimilation.
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t = 7 dayt = 14 day t = 21 day t = 28 day t = 35 day t = 42 day
t = 49 day t = 56 day t = 63 day
Summary Setup a Northwest Pacific Ocean Circulation Model
Developed an Ensemble Kalman Filter Performed an identical twin
experiment to evaluate the performance of Ensemble Kalman Filter
Assimilation of SST Work in Progress Assimilation of Subsurface
Temperature Data from CDT, XBT and ARGO Floats Future Plan Setup a
higher resolution model and assimilate Sea Surface Height in order
to resolve eddies and current meandering Provide initial condition
and open boundary data for (3km and/or 1km resolution) coastal
ocean models
Slide 50
Thank you ! Assimilation of Sea Surface Temperature into a
Northwest Pacific Ocean Model using an Ensemble Kalman Filter