1 COAS-CIOSS Coastal Ocean Modeling Activities Coastal Ocean Modeling Studies at COAS are focused on:  Wind-driven upwelling and downwelling [Allen et al.] - flow-topography effects [Gan, Kuebel Cervantes, Whitney, Kurapov et al.] - nonlinear evolution of frontal instabilities [Durski et al.] - ocean-atmosphere feedbacks [N. Perlin, Skyllingstad, Samelson, CIOSS] - bio-physical interactions [Spitz et al.]  Coastal / interior ocean interactions in the Coastal Tranzition Zone (CTZ) [B.-J. Choi (CIOSS), S. Springer et al.]  Data assimilation [Kurapov, Allen, Egbert, Miller]  Real-time ocean prediction [Erofeeva, Kurapov et al. (CIOSS)]

2 How does coastal ocean modeling help address CIOSS goals?  Dynamical interpretation of physical features apparent in satellite data (on the shelf and in the coastal transition zone)  Assimilation of satellite data, together with other data (providing dynamically based interpolation and mapping of the satellite data; filling gaps in space and time)  Analysis of physical models, integrated with observations, - to improve scientific understanding of the ocean dynamics, and - to predict the ocean dynamics

3 Coastal Ocean Dynamics off Oregon: HF radars () HF radars (Kosro) Moorings (ADP, T, S: ) Levine, Kosro, Boyd) Model: space-time continuous solutions (velocity, T, S, turbulence quantities) Movie: surface T and tracers (development of upwelling June 2001, Princeton Ocean Model solution constrained by assimilation of COAST data) COAST Observing System, summer 2001

4 Topographic effects [Kurapov et al., JPO, 2005]:  B east of Stonewall Bank  B at 45N, H=98 m On the mid-shelf, bottom mixed layer thickness is small at 45N, large at 44.4N in response to upwelling 44.4N: As a result of bottom Ekman transport convergence, thinner surface BL, thicker bottom BL Turbulent KE in response to the upwelling event (day 170, 2001): Depth, 0 – 100 m lon, W At 45N At 44.4N

5 Coupled Ocean-Atmosphere Modeling (N. Perlin, Skyllingstad, Samelson, with support from CIOSS and ONR): Accurate representations of coastal upwelling processes must include ocean-atmosphere interactions on short temporal and horizontal scales COAMPS: wind, heat ROMS: upwelling response (T) wind stress heat flux… Effect of coupling on atmos. eddy visc. … cold water [N. Perlin et al. JPO, submitted] Initial value problem: run from rest for 72 h

6 Dynamical coupling of the coastal ocean and California Current System (CCS) flows through the Coastal Tranzition Zone (CTZ) [B.-J. Choi (GLOBEC-NOAA, CIOSS), S. Springer (NOPP)] - Unstable, separating coastal flows feed into the CCS - Mesoscale eddies (CCS) affect variability in the coastal waters Nesting: -9 km NCOM-CCS Atm. forcing: COAMPS (16-km) [J. Kindle (NRL)] -3 km ROMS-CTZ Open boundary conditions: appropriate for advective currents, coastal trapped waves, tides, Rossby waves, Columbia R. Can nesting improve the prediction of coastal currents? Can data assimilation help? SSH (5/31/02): NCOM ROMS, NCOM m

7 SST (5/31/02): higher spatial variability in ROMS SST NCOM ROMS, NCOM Surface Salinity: inclusion of Columbia R. in ROMS

8 Model-data comparisons: NCOM vs. alongtrack SSH altimetry Even though NCOM-CCS assimilates SSH using “nudging”, the data are not fit particularly well in the CTZ Room for improvement: assimilate alongtrack SSH, together with other obs. in the CTZ domain model Demeaned alongtrack SSH: T/P, NCOM SSH RMS diff. (NCOM – Altim.), 2002 SSH correlation (NCOM, Altim.), longitude along satellite track (cm) cm

9 Data assimilation (DA) [Kurapov, Allen, Egbert, Miller, ONR] Approach: complicated, fully-nonlinear model + simple DA simplified models + rigorous variational DA Merger of approaches: nonlinear models + variational DA Princeton OM + sequential Optimal Interpolation: assimilate HF radar (Oke et al. JGR 2002), moored velocity data (Kurapov et al. 2005abc) Assimilation of moored ADP data: - improves model accuracy at a distance of 90 km in the alongshore dir. - improves prediction of T, S, SSH near coast, near- bottom turbulent dissipation

10 Variational DA: fit the model solution to the observations over a given time interval (by correcting errors in the inputs) Minimize the penalty function: J(u) = || model error || 2 + || obs. error || 2 Obtain information on the source of model error Utilize (compute) state-dependent model error covariance Assimilate observations (incl. satellite SSH, SST, HF radar) w/out pre- processing the observations into maps Variational DA: utilizes tangent linear and adjoint models, algorithmically complicated, computationally challenging Our path: representer-based optimization (following methodology developed by Bennett, Egbert, et al.) The nonlinear optimization problem is approached as a series of linearized problems. Each linearized problem: search for the solution correction in a relatively small subspace spanned by K representer solutions, where K is the number of observations (still, no need to compute all the representers)

11 Tests of the representer-based method (with the shallow-water model, describing flows in the near-shore surf zone): - assimilation in presence of instabilities, intrinsic eddy interactions - correction of forcing, open boundary conditions Equilib. shear wave regime (T=60 min)More irregular regime (T=5 min) True solution ( shown is vorticity) (Prior = 0) DA solution: -assimilate time series of , u, v at 32 pnts - correct IC, forcing Unsteady solution in response to steady forcing Click on frame to play movie (left for 60 min, right for 5 min).

12 Real-time Oregon coastal simulation system (Erofeeva, Kurapov, Samelson, Egbert, CIOSS) ROMS (  x = 2 km), forced with 3-day atmospheric NAM forecast: daily update Model data comparisons: SSH, SST (incl. monthly climatologies), HF radar data HF radar (Kosro) forecast (5/11/06) Additional QC: coordinated with the NOAA-funded OrCOOS pilot project (J. Barth, R. K. Shearman) This looks way too good… somebody must be cheating…

13 1. Research involving coastal ocean modeling has been focused on flow/topography, ocean/atmosphere, CCS/shelf flow interactions. 2.Variational DA has the potential of providing new versatile tools for synthesis of satellite, in-situ and land-based HF radar observations. 3. Work to advance the real-time Oregon Coastal Simulation System will be leveraged by efforts on ongoing GLOBEC, NOPP, and ONR research projects - improved ROMS configuration - DA (alongtrack SSH, HF radar) 4. The real-time modeling system will become an integral part of the emerging OrCOOS, facilitating interactions within COAS research community SUMMARY: