1. NOPP Project: Boundary conditions, data assimilation, and predictability in coastal ocean models OSU: R. M. Samelson (lead PI), J. S. Allen, G. D. Egbert, A. Kurapov, R. N. Miller NRL: J. C. Kindle NCAR: C. Snyder Objectives: Determine impact of open ocean boundary conditions from GODAE Pacific Ocean models on Oregon coastal ocean models by comparison with in situ measurements Determine impact on Oregon coastal ocean simulations of assimilating satellite remote sensing observations Quantify uncertainty and predictability in coastal ocean simulations + Interactions with ongoing ONR, CIOSS, and GLOBEC funded projects on coastal ocean/atmosphere modeling and data assimilation

2. Coastal Model daily ave. surface T, velocities (7 Sept. 2005). Using ROMS at 2 km resolution, forced with output from atmospheric ETA model AVHRR Satellite SST (24 Aug. 2003) Isobaths: 100 and 200 m. Spatial variability and flow-topography interactions in coastal flows: Stronger interactions between coastal and interior oceanic flows in the observed fields than in the coastal model Improvements in coastal transition zone (CTZ) modeling require nesting in larger scale models

3. The SSH snapshot from the 1/12 o resolution Pacific HYCOM (15 July 2001) [ J. Metzger, NRL ]. The boundary of the regional West Coast HYCOM (also NCOM-CCS) ROMS-based Oregon coastal model in white Interaction of the shelf flows and interior ocean seen in model SSH

4. Model improvement in the CTZ should also result from assimilation of SSH, SST, and long-range HF radar data 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) GLOBEC study: analysis of CCS model simulation Model solution for 2000 provided by E. Curchitser, currently being analyzed by CIOSS-supported post-doc B. J. Choi

5. Location of time series observations to be used for assimilation and validation Black contours: H= 100, 200, and 1000 m Daily ave. HF radar surface currents, (courtesy of P. M. Kosro at OSU).

6. Linearized models + rigorous (variational) DA 1) Theoretical models: help formulate model error statistics for practical applications [Scott et al., JPO, 2000, Kurapov et al., Mon. Wea Rev., 2002] 2) 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] “Dual Approach” 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 Present focus: merger of these approaches: Development of DA methods for the Coastal Ocean (research supported by ONR)

7. Advantages of variational DA: the study of M 2 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 HF ADP Representer-based minimization optimally projects surface observational information to 3D (Improvement is verified to be obtained at ADP site) Day, 1998 Depth Tidal ellipses of the horizontal current (for a series of overlapped 2-week time windows) depth-ave Deviations from depth-ave Validation ADP DA no DA

8. Time-ave baroclinic KE: Surface Bottom lat 45.65Nlat 45.55N Inverse solutions provide a uniquely detailed picture of the spatial and temporal variability of the M2 internal tide Surface tidal ellipses Day 139 Deviations from depth-ave (gray ellipses rotate CW) Depth-ave (white ellipses rotate CCW)

9. 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 () HF radars (Kosro) Moorings (ADP, T, S: ) Levine, Kosro, Boyd)

10. Optimal Interpolation (OI) Data assimilation: - sequential, optimal interpolation (OI) - correction is added in small increments every time step matrix matching observations to state vector - correction only to u : -correction term is present in momentum equations -however, equations for T, S, q 2, q 2 l are dynamically balanced (which facilitates their term balance analysis) - Approximate gain matrix obtained from an ensemble of model runs Time-invariant gain matrix ||Error|| model w/out DA DA forecast analysis Time

11. Effects of DA: improvement in near-bottom currents alongshore velocity at NH10 mooring [Kosro] close to surface close to bottom model-data corr.: 0.52 (no DA)  0.83 (DA) rms error: 8.1 (no DA)  4.4 cm s -1 (DA) Assimilation sites:

12. Effects of ADP velocity DA: improvement in near-shore SSH time series Assimilation of velocity observations in shelf circulation models can improve accuracy of SSH maps in the coastal zone, where altimetry is not available 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 )

13. Optimal Interpolation: limitations OI corrects the ocean state, not forcing  limited control over source of error OI assumes time-invariant forecast error covariance, used to compute the gain matrix. State-dependent covariance is needed to predict events. 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 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. Our ongoing research is focused on GIM assimilation into nonlinear coastal models Variational, representer based, generalized inverse method (GIM)