hernan g. arango, rutgers university ([email protected]) tal ezer, pricenton university...

Hernan G. Arango, Rutgers University ([email protected])

Tal Ezer, Pricenton University ([email protected])

FTP File: TOMS.tar

A Community Terrain-following Ocean Modeling

System

Bennett et al. (FNMOC; OSU)

Chassignet / Iskandarani et al. (RSMAS)

Cornuelle / Miller (SIO)

Geyer (WHOI)

Hetland (TAMU)

Lermusiaux (Harvard)

Mellor (Pricenton)

Moore (U. Colorado)

Shchepetkin (UCLA)

Signell (SACLANT; USGS)

COLLABORATORS

Chao / Song (JPL)

Preller / Martin (NRL)

Naval Operational Community

POM Ocean Modeling Community

ROMS / SCRUM Ocean Modeling Community

OTHER COLLABORATORS

To design, develop and test an expert ocean modeling system for scientific and operational applications

To support advanced data assimilation strategies

To provide a platform for coupling with operational atmospheric models (like COAMPS)

To support massive parallel computations

To provide a common set of options for all coastal developers with a goal of defining an optimum coastal/relocatable model for the navy

OBJECTIVES

Use state-of-the-art advances in numerical techniques, subgrid-scale parameterizations, data assimilation, nesting, computational performance and parallelization

Modular design with ROMS as a prototype

Test and evaluate the computational kernel and various algorithms and parameterizations

Build a suite of test cases and application databases

Provide a web-based support to the user community and a linkage to primary developers

APPROACH

“The complexity of physics, numerics,data assimilation, and hardware technology should be transparent

to the expert and non-expertUSER”

CHALLENGE

Free-surface, hydrostatic, primitive equation model

Generalized, terrain-following vertical coordinates

Boundary-fitted, orthogonal curvilinear, horizontal coordinates on an Arakawa C-grid

Non-homogeneous time-stepping algorithm

Accurate discretization of the baroclinic pressure gradient term

High-order advection schemes

Continuous, monotonic reconstruction of vertical gradients to maintain high-order accuracy

TOMS KERNEL ATTRIBUTES

Dispersive Properties of Advection

/2/4 3/4

kx

1/2

1

3/2

2

5/2

K(k

) • x

2

4

610

8

ParabolicSplines

Vs

FiniteCentered

Differences

Horizontal mixing of tracers along level, geopotential, isopycnic surfaces

Transverse, isotropic stress tensor for momentum

Local, Mellor-Yamada, level 2.5, closure scheme

Non-local, K-profile, surface and bottom closure scheme

TOMS SUBGRID-SCALE PARAMETERIZATION

Air-Sea interaction boundary layer from COARE (Fairall et al., 1996)

Oceanic surface boundary layer (KPP; Large et al., 1994)

Oceanic bottom boundary layer (inverted KPP; Durski et al., 2001)

TOMS BOUNDARY LAYERS

Boundary Layer Schematic

Longwave

Shor

twav

e

Evap

OHH

OHH

1. ABL2. SBL3. BBL

4. WCBL

Air-Sea interaction boundary layer from COARE (Fairall et al., 1996)

Oceanic surface boundary layer (KPP; Large et al., 1994)

Oceanic bottom boundary layer (inverted KPP; Durski et al., 2001)

TOMS BOUNDARY LAYERS

Wave / Current / Sediment bed boundary layer (Styles and Glenn, 2000)

Sediment transport

Lagrangian Drifters (Klinck, Hadfield)

Tidal Forcing (Hetland, Signell)

TOMS MODULES

Gulf of Maine M2 Tides

Surface

Elevation(m)



TOMS MODULES

River Runoff (Hetland, Signell, Geyer)

5 10 15 20 25

Distance (km)

-10

-15

-25

-20

-5

Dep

th (

m)

30

25

20

15

10

5

Sal

init

y (P

SS

)

Hudson River Estuary



River Runoff (Hetland, Signell, Geyer)

TOMS MODULES

Biology Fasham-type Model (Moisan, Shchepetkin)

EcoSim Bio-Optical Model (Bissett)

Systematic evaluation of numerical algorithms via robust test problems

Data/Model comparisons

Study optimal combination of algorithmic options for various coastal applications

Documentation of testing procedures

TOMS TESTING

Modular, efficient, and portable Fortran code (F77+, F90)

C-preprocessing managing

Multiple levels of nesting

Lateral boundary conditions options for closed, periodic, and radiation

Arbitrary number of tracers (active and passive)

Input and output NetCDF data structure

Support for parallel execution on both shared- and distributed -memory architectures

TOMS CODE DESIGN

Coarse-grained parallelization

TOMS PARALLEL DESIGN

}

} Nx

Ny

PARALLEL TILE PARTITIONS

8 x 8

Coarse-grained parallelization

TOMS PARALLEL DESIGN

Shared-memory, compiler depend directives MAIN (OpenMP standard)

Distributed-memory (MPI; SMS)

Optimized for cache-bound computers

ZIG-ZAG cycling sequence of tile partitions

Few synchronization points (around 6)

Serial and Parallel I/O (via NetCDF)

Efficiency 4-64 threads

Nudging

Optimal Interpolation (OI)

Tangent linear and Adjoint algorithms

4D VARiational data assimilation (4DVAR) and Physical Statistical Analysis System (PSAS) algorithms

Inverse Ocean Modeling System (IOMS)

Ensemble prediction platform based on singular value decomposition

Error Subspace Statistical Estimation (ESSE)

TOMS DATA ASSIMILATION

Historical, Synoptic, Future in

Situ/Remote Field/Error Observations

d0R0

FieldInitialization

Central Forecast

Sample Probability

Density

Mean

SelectBest

Forecast

Shooting

ESSE Smoothing viaStatistical Approximation

MinimumError

Variance

Within ErrorSubspace

(Sequential processing ofObservations)

MeasurementModel

A PosterioriResidulesdr (+)

Performance/AnalysisModules

OA viaESSE

GriddedResidules

Synoptic Obs

Measurement Model

Measurement Error

Covariance

^

cf(-)^

0

Options/Assumptions

Most Probable Forecast

mp(-)^

EnsembleMean

q{j^

Adaptive Error

SubspaceLearning

ConvergenceCriterion

Continue/StopIteration Breeding

PeripheralsAnalysisModules

Normalization

SVDp

Continuous Time Model Errors Q(t)

ScalableParallel

EnsembleForecast

+PerturbationsError SubspaceInitialization

1

j

q

1

j

q

^

^

^

uj(o,Ip)with physicalconstraints

+

(+)^

E(+)(+)

E0

(+)^

Ea(+)a(+)

FieldOperationAssumption

Key

(-)^

E(-)(-)

-+

+

+

-

+-

---

-

+

+

+

dC(-)

Data Residuals

^

+

+

+

--

+/-+

j=1

j=q

+

ESSE FlowDiagram

Density Jacobian Class (Blumberg and Mellor, 1987; Song 1998; Song and Wright 1998)

More Accurate

Error vanishes with linear density profiles

Pressure Jacobian Class (Lin 1998; Shchepetkin and McWilliams, 2001)

JEBAR consistent

Conserve Energy

PRESSURE GRADIENT FORCE

Seamount Test Case

(64 x 64 x 20) dx = dy = 8 km

Models with 2nd order advection schemePOM ROMS

Second Order Advection Scheme

SurfaceElevationAnomaly

StreamFunctionAnomaly

Advection Schemes in ROMS (Seamount Case)

V

Second OrderCentered

Third Order Upstream Bias

Fourth Order Centered

Pressure Gradient Errors

POM(6th order)

U (

cm/s

)V

(cm

/s)

ROMS

POM

X (km)

Relative CPU per time step

Per

cen

tage

Build TOMS from ROMS prototype Mellor-Yamada, level 2.5 Passive and active open boundary conditions Tidal forcing River runoff Lagrangian drifters Data assimilation

Inter-comparison between POM and ROMS Evaluation of time-stepping, advection, and

pressure gradient algorithms

Initial development of TOMS web site

RESULTS (YEAR 1)

Initial web page: www.aos.princeton.edu/WWWPUBLIC/ezer/TOMS

To Be Determined !!!

Potential Users:

NAVO

FNMOC

NOAA

USCG

TRANSITION PATHS

Chassignet et al., 2000: Damee modeling review

Ezer, 2000: Mixed-layer evaluation

Ezer and Mellor, 2000: POM Damee application

Haidvogel et al., 2000: ROMS Damee application

Malanotte-Rizzoli et al., 2000: ROMS Damee

Mellor, 2001: Improved turbulence scheme

Mellor et al., 2001: Generalized vertical coordinate

PUBLICATIONS

hernan g. arango, rutgers university ([email protected]) tal ezer, pricenton university...

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