Data Assimilation in Coastal Models – Moving toward IOOS and Prediction
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Data Assimilation in Coastal Models – Moving toward IOOS and Prediction J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, R. M. Samelson College of Oceanic and Atmospheric Sciences, Oregon State University. Projects with support through CIOSS:

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Data Assimilation in Coastal Models – Moving toward IOOS and Prediction

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Data assimilation in coastal models moving toward ioos and prediction

Data Assimilation in Coastal Models – Moving toward IOOS and Prediction

J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, R. M. Samelson

College of Oceanic and Atmospheric Sciences, Oregon State University

  • Projects with support through CIOSS:

  • Real-time Oregon coastal simulation system (Pilot project)

  • (PIs: R. M. Samelson, G. D. Egbert, A. L. Kurapov; associate: S. Erofeeva)

  • US-GLOBEC-NEP Phase IIIa (CCS): Effects of meso- and basin scale variability on zooplankton populations in the California Current System using data-assimilative, physical/ecosystem models, 2005-2008.

  • (PIs: J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, D. B. Haidvogel (Rutgers U.), T. M. Powell (UC Berkley), E. N. Curchitser (Columbia U.))

  • CIOSSprovides partial support for a post-doctoral research associate

+ Interaction with ongoing ONR, NOPP, NSF funded projects on coastal ocean/atmosphere modeling and data assimilation


Data assimilation in coastal models moving toward ioos and prediction

Coastal Ocean Data Assimilation:Long Term Goals/Vision

  • Develop and utilize advanced modeling and data assimilation techniques to improve scientific understanding of oceanic dynamic processes on the continental shelf and interactions of the shelf flows with the interior ocean

  • Transfer new computational technologies into operational nowcast/forecast systems

With CIOSS support:Real-time Oregon coastal simulation system(OCS)

OCS, present implementation:

Oceanic model: ROMS (Dx = 2 km), periodic channel domain (A. Kurapov, S. Erofeeva)

Atmospheric model: ETA (run at OSU by R. M. Samelson and P. Bourbur)

OCS, future developments:

- improve ocean model formulation (open boundaries, nesting)

- data assimilation capability (improve forcing and boundary conditions, assimilate SSH, SST, HF radar surface currents)


Data assimilation in coastal models moving toward ioos and prediction

GLOBEC study: analysis of NEP model simulation

  • Focus on coastal transition zone (CTZ), altimetry + long range HF radar assimilation

  • Model solution for 2000 provided by E. Curchitser, currently being analyzed by CIOSS-supported post-doc B. J. Choi)

ROMS model

Dx=3 km

Initial and boundary conditions: from 10 km NEP model

Atmospheric forcing: NCEP reanalysis (2.5 degree)

Extent of regional model (apprx. 650 250 km) for initial tests on nesting, data assimilation

Model bathymetry


Data assimilation in coastal models moving toward ioos and prediction

Comparison between model and gridded altimetry products

Time-averaged SSH anomaly (August 2000)

Tracks shown: T/P, ERS

Model: includes spatial structures not resolved by gridded product

Gridded altimetry (AVISO)

  • By assimilation of along-track altimetry from multiple satellites we can:

  • Improve mapping of SSH by incorporating dynamics

  • Increase understanding of interactions between coastal flows and oceanic meso-scale eddies


Data assimilation in coastal models moving toward ioos and prediction

A critical need for coastal modeling: Learn to assimilate SSH, SST, and long-range HF radar data to improve boundary conditions

Model SST and surf. currents (August 2000):

Coastal ocean interacts with interior ocean

Separation near Heceta Bank, Cape Blanco

Upwelling is weaker than observed (should be improved using higher resolution winds)


Data assimilation in coastal models moving toward ioos and prediction

Development of DA methods for the Coastal Ocean (research supported by ONR)

“Dual Approach”

  • Linearized models + rigorous (variational) DA

  • Theoretical models: help formulate model error statistics for practical applications [Scott et al., JPO, 2000, Kurapov et al., Mon. Wea Rev., 2002]

  • Internal tide, HF radar surface velocity data [Kurapov et al., JPO, 2003]

Fully nonlinear model + suboptimal, sequential DA (Optimal Interpolation)

1) HF radar surface velocity data [Oke et al., JGR, 2002]

2) moored ADP velocities [Kurapov et al., JGR, 2005a, 2005b, JPO, 2005]

Present focus: merger of these approaches:

Application of tangent linear and adjoint ROMS for variational DA

Barotropically unstable jet in a channel[Kurapov and Di Lorenzo, 2005]

Forced-dissipative flows in the nearshore: ongoing research

Three-dimensional, stratified flows on shelf: ongoing research


Data assimilation in coastal models moving toward ioos and prediction

Advantages of variational DA: the study of M2 internal tide off Oregon

  • HF radar data for summer 1998 (provided by P. M. Kosro) are assimilated in a linear frequency-domain model[Kurapov et al. JPO, 2003]

  • DA corrects open boundary (OB) baroclinic tidal currents

  • Inverse solution minimizes penalty function: J(u)= || OB error ||2 + || data error ||2

Tidal ellipses of the horizontal current (for a series of overlapped 2-week time windows)

Representer-based minimization optimally projects surface observational information to 3D

(Improvement is verified to be obtained at ADP site)

depth-ave

ADP

HF

Deviations from depth-ave

Validation ADP

no DA

Day, 1998

HF

Depth

DA


Data assimilation in coastal models moving toward ioos and prediction

Inverse solutions provide a uniquely detailed picture of the spatial and temporal variability of the M2 internal tide

Surface tidal ellipses Day 139

Time-ave baroclinic KE:

Bottom

Surface

lat 45.65N

lat 45.55N

Depth-ave

(white ellipses rotate CCW)

Deviations

from depth-ave

(gray ellipses rotate CW)


Data assimilation in coastal models moving toward ioos and prediction

Experience assimilating data into the fully nonlinear, primitive eqn. model of wind-driven shelf circulation (studies of summer upwelling)

Dynamics: Princeton Ocean Model (free surface, nonlinear, primitive eqn., w/ turbulence parameterization [Mellor & Yamada 1982])

-Realistic bathymetry

-Boundary conditions: periodic (south to north)

-Forcing: alongshore wind stress and heat flux

HF radars (Kosro)

Moorings (ADP, T, S:

Levine, Kosro, Boyd)


Data assimilation in coastal models moving toward ioos and prediction

Optimal Interpolation (OI) Data assimilation:

- sequential, optimal interpolation (OI)

- correction is added in small increments every time step

matrix matching observations to state vector

Time-invariant gain matrix

- correction only to u:

-correction term is present in momentum equations

-however, equations for T, S, q2, q2l are dynamically balanced (which facilitates their term balance analysis)

- Approximate gain matrix obtained from an ensemble of model runs


Data assimilation in coastal models moving toward ioos and prediction

Effects of ADP velocity DA: improvement in near-shore SSH time series

comparison with coastal tide gauge data near Newport

obs, no DA, DA

SSH, surf v, no DA

SSH, surf v, DA

surf v, HF radar [Kosro]

Flow control over Stonewall Bank

(Day 166, 2001)

Assimilation of velocity observations in shelf circulation models can improve accuracy of SSH maps in the coastal zone, where altimetry is not available


Data assimilation in coastal models moving toward ioos and prediction

Effects of ADP velocity DA: improvement in near-surface salinity transport

ADP assimilation

No ADP assim.

Observed, days 162-164

(SeaSoar - Barth et al.)

to introduce Columbia River, salinity is assimilated at 45N

Time series of salinity at 2.4 m, 44.2N:

obs, DA, no DA

S<32 psu: effect of Columbia R.


Data assimilation in coastal models moving toward ioos and prediction

Effects of ADP velocity DA: improvement in the level and temporal variability of near-bottom turbulent dissipation rate (e) and bottom stress

Turbulence Observations: Moum et al.

12 transects, days 139-148 (2001)

e

transect 1

Area-ave. e

Area-ave. bottom stress

yearday, 2001


Data assimilation in coastal models moving toward ioos and prediction

Optimal Interpolation: limitations

OI corrects the ocean state, not forcing  limited control over source of model error

OI assumes time-invariant forecast error covariance (Pf), used to compute the gain matrix  satisfactory performance on average over a season, but possibly difficulties predicting events (instabilities, relaxation from upwelling to downwelling, etc.). State-dependent covariance is needed.

Observations (such as satellite SSH, SST, HF radar) will generally have to be processed into maps (without spatial or temporal gaps) before using OI-DA

Variational, representer based, generalized inverse method (GIM) has potential of resolving these and some other deficiencies of OI.

Methodology has been developed for using GIM efficiently with nonlinear oceanic models [Chua and Bennett, 2001]. This technology is yet to be tried in the context of coastal ocean circulation modeling.

To use GIM, tangent linear and adjoint models have to be developed.


Data assimilation in coastal models moving toward ioos and prediction

Ongoing research: variational representer-based assimilation into nonlinear coastal models

Tangent Linear (TL) and Adjoint (ADJ) of ROMS have been developed by ROMS AD Group (A. Moore et al.)

  • We are testing these tools as they become available:

  • Use available version of TL-AD ROMS (initial value problem – see poster)

  • Construct our own shallow water TL and AD codes to learn details of GIM (forced-dissipative cases)

  • Transition to use of full ROMS TL/ADJ as these become available over the next year or so

  • Some research issues:

  • Assimilation in presence of frontal instabilities (nonlinearity constrains growth of instabilities in the fully nonlinear model, but not so in the TL model)

  • Proper linearization of open boundary conditions (e.g., radiation conditions with non-smooth switching from inflow to outflow conditions)


Coastal ocean atmosphere boundary layer interactions

Coastal Ocean-Atmosphere Boundary Layer Interactions

R. Samelson, E. Skyllingstad, N. Perlin, P. Barbour

College of Oceanic and Atmospheric Sciences

Oregon State University


Coastal ocean atmosphere boundary layer interactions long range objectives

Coastal Ocean-Atmosphere Boundary Layer InteractionsLong Range Objectives

  • Improve our ability to understand and predict environmental conditions in the coastal zone, especially with regard to the use and augmentation of satellite observations of wind stress

  • Improve understanding of the processes that link wind stress variations to sea-surface temperature variability and ocean circulation patterns.


Data assimilation in coastal models moving toward ioos and prediction

Coastal Ocean Data Assimilation:Long Term Goals/Vision

  • Develop and utilize advanced modeling and data assimilation techniques to improve scientific understanding of oceanic dynamic processes on the continental shelf and interactions of the shelf flows with the interior ocean

  • Transfer new computational technologies into operational nowcast/forecast systems


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