1

COAS-CIOSS Coastal Ocean Modeling ActivitiesCOAS-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: 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 12-22 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 a

t 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):

Dep

th,

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-CCSAtm. 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

0.3 0.1 -0.1 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.), 2002

-130 -126-128 -124longitude along satellite track

0

8

4

(cm)

0

1

-0.5

40 -5 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: