Variational Assimilation of HF Radar Surface Currents into the Coastal Ocean Circulation Model off Oregon

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Variational Assimilation of HF Radar Surface
Currents into the Coastal Ocean Circulation Model
off Oregon
Peng Yu in collaboration with Alexander Kurapov,
Gary Egbert, John S. Allen, P. Michael
Kosro College of Oceanic and Atmospheric
Sciences Oregon State University
Supported by ONR
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• Complicated dynamics on the shelf in the
• coastal transition zone (CTZ)
• Strong upwelling season
• Modeling sensitive to many factors (model
  resolution, horizontal eddy viscosity,
  bathymetry, boundary conditions, forcing)
• Use data assimilation to improve prediction,
  forecasting, and scientific understanding of
  shelf and CTZ flows

6-km, visc10 m2/s
Model details Regional Ocean Modeling System
(ROMS) - 6km horizontal resolution and 15
vertical level - NOOA -NAM wind heat flux -
NCOM-CCS boundary conditions (Shulman et al., NRL)
(shown SST Jul. 20, 2008)
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Summer 2008 available observations
Bathymetric contours 1000 and 200 m)

• - HF radar surface velocities (daily maps,
  provided by PM Kosro, OSU)
• Combination of several standard and long-range
  radar provides time-series info about shelf,
  slope and CTZ flows
• SSH along track altimetry (Jason, Envisat)
• satellite SST maps (D. Folley, NOAA CoastWatch)
• gliders (T and S sections, once every 3 days, J
  Barth and R. K. Shearman, OSU) 3D information

How does each of these data types contribute to
data assimilation?
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• Variational (representer-based) data assimilation
• in a series of 3-day time windows, June 1 July
  30, 2008
• In each window,
• correct initial conditions (use tangent linear
  adjoint codes AVRORA, developed at OSU, Kurapov
  et al., Dyn. Atm. Oceans, 2009)
• run the nonlinear ROMS for 6 days (analysis
  forecast)

assim (TLADJ AVRORA)
forecast (NL ROMS)
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The representer-based DA system
the representer function
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Indirect method (Egbert et al. 1994 Chua and
Bennett, 2001)
Forecast model

• Indirect method is computationally more efficient
• Conjugate Gradient method
• Preconditioned Conjugate Gradient

Adjoint model
Tangent Linear model
Task Parallel
.
Precondition
.
Conjugate Gradient (5-10 steps)
(Egbert, 1997)
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Initial Condition Error Covariance (Dynamically
balanced) multivariate u, v, SSH, T, S
geostrophy, thermal-wind Implement the balance
operator A (Weaver et al. 2005)
Adj solution at ini time
univariate covariance for mutually uncorrelated
fields s

• A
• Uncorrelated fields error in T and
  depth-integrated transport (uH, vH)
• S (using constant T-S relationships)
• horizontal density gradients
• vertical shear in u, v (thermal wind
  balance)
• SSH (2nd order ellipitic eqn.)
• u, v (surface current in geostrophic balance
  with SSH)

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Observed and prior model SST and surface currents
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Initial test with one 3-day assimilation window
(balanced covariances better in SST forecast)
Surface Velocity
SST (not assimilated)
RMSE
Correlation
Analysis
Forecast
Analysis
Forecast
SST data provided by D. Foley, NOAA CoastWatch
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Same experiment extend the forecast to 15days
SST (not assimilated)
Surface Velocity
RMSE
Balanced better
Correlation
Analysis
Analysis
Forecast
Forecast
SST RMSE and correlation are improved for 15
days, after the 1st assimilation cycle
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60-day assimilation (June 1-July 30, 2008 20
assimilation windows) Both Surface velocity and
SST are improved
Surface Velocity
SST (not assimilated)
RMSE
Correlation
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Model data comparison Surface currents
(assimilated) and SST (not assimilated)
Assimilation of HF radar surface currents
improves the geometry of the upwelling SST front
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The time-averaged sea surface currents field
more uniform cross-shore transport than in prior
Observation
Prior
Forecast
Analysis
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The mean and variance ellipses of the model-data
difference
Mean Obs-Forecast
Mean Obs-Analysis
Mean Obs-Prior
Variances
Variances
Variances
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Assimilation of HFR data improves SSH, compared
to along-track altimetry (not assimilated) in
the area covered by the HF Radar
Verification SSH, prior, HF radar velocity
assimilation
Data coverage
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Cont. (another pass)
Verification SSH, prior, HF radar velocity
assimilation
Data coverage
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Comparison against Hydrographic data
Analysis (balanced)
Analysis (Imbalanced)
Observation
Prior
-The assimilation of the HF Radar surface
currents data improves the density structure in
the hydrographic sections south of Cape Blanco in
the separation zone
NH
CC
RR
Data provided by Bill Peterson and Jay Peterson)
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Summary

• The representer-based data assimilation system
  improves the forecast of the model variables
  (e.g. SST, surface currents, SSH, density)
• The assimilation of unique set of long-range HF
  radar observations has a positive impact on the
  area of the shelf, slope and part of the open
  ocean
• Assimilation of the HF Radar
• radial component might be
• advantageous
• A combination of different types
• of observations is being pursued