Estimating and Predicting Ocean Currents in the U.S. coastal oceans

John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*,

Hongchun Zhang*, Peggy Li, Quoc Vu

NASA Jet Propulsion Laboratory

*Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles

Ocean Currents and End User Feedback Workshop, May 5-6, 2011, Atlanta 1

DataAssimilation

Model

Products

UsersObservations

Feedback

Forecasting

Ocean Hindcast/Nowcast/Forecast

Ocean observing is rapidly expanding, ocean models are maturing; To what extent can the regional oceans be predicted from synoptic weather (days) to climate (seasons, interannual, decadal) time scales?

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A Portable, Data-Assimilative Coastal Ocean Nowcast/Forecast/Hindcast System

- Based on the ROMS regional ocean model A typical configuration has a horizontal resolution of a few km or less,

and extends several hundred kilometers offshore

Nowcasts typically generated every 6 hours, with a daily 48 or 72 hour forecast

- A multi-scale 3DVAR data assimilation scheme

Large and small spatial scales separately assimilated

Scale-dependent error covariances

Scale-dependent dynamic balance constraints

Typical data assimilated includes satellite SSTs, HF radar surface currents

and glider/mooring temperature and salinity vertical profiles

- Tides: Oregon State University global tidal forcing

- Atmospheric Forcing: Regional atmospheric models (WRF, NAM)

- Ensemble Forecasting methodology

- Interactive trajectory tool, real-time execution is fully automated3

U. S. Coastal Regions where the system has been applied

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15-km 5-km 1.5-km

Multi-level Nested Regional Ocean Modeling System (ROMS)

A multi-scale (or “nested”) ROMS modeling approach has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by satellites and coastal HF

radars in a way that is computationally efficient enough to allow real-time operations.

5

.

x is obtained by minimizing the Cost Function

J = (x)T B-1 (x) + (h x-y)T R-1 (h x-y)

xa = xf + x

3DVAR Data Assimilation(3-dimensional variational)

x: model (with error); xft+1=M(xa

t)

f: forecast (model alone)a: analysis/nowcast (with data)

y: observation (with error); h: map model to data

References: Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090.Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation schemefor the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042.

Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation scheme and its application to coastal

oceans. Quart. J. Roy. Meteorol. Soc., submitted.

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7

3DVAR Data Assimilation With Geostrophic Constraint

xuv

xuvf Gdx

TSF

adx

a yc

Min[(Xuvo-Xuv

f)2]

uvG

TSGeostrophic Balance

HF Radar

Current Obs.

Geostrophic vs. Non-geostrophic

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3DVAR Data Assimilation With Hydrostatic Constraint

Min[(Xo - Xf)2]

Hydrostatic Balance dxzS Pdx

TS

xV

xVf Pdx

TSdx

aV

Satellite

Altimetric

Sea Level Obs.

Steric vs non-steric

Satellite SST/SSH HF Radar

LR-

3DVAR

xLf

Forecast

Smoothed

x f

Start

HR-

3DVAR

Low-Res

Increment La xa x f dx a

xLa x f dx

La

End

Glider/Argo/Mooring

Smoothed

Multi-Scale 3DVAR Data Assimilation

High-Res

Increment dx a

t+1

Low-Res. Observations High-Res. Observations

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Impact of Surface Current Data Assimilation on

Nowcast

RMS Correlation

ROMS w/o

HF radar data

ROMS with

HF radar data1

0

Impact of Surface Current Data Assimilation on

Forecast

ROMS forecast w/o

sfc current data Persistence

ROMS forecast with

sfc current data

1

1

12

HF Radial Current Data Assimilation

Quality Control:

STD<10 cm/sec

MapError<0.95

JPL/ROMS

DAS

L0 10km, 40 layersL1 3.3.km, 40 layersL2 1.1km, 40 layers

Atmospheric Forcing:L2: UAA, 4km WRF

L1, L0: 0.5o GFS

Tides forced on lateral boundaryof L0 domain byOSU global tide model output.

Gulf of Alaska/Prince William Sound Configuration

1

3

ROMS vs. Independent Data:Drifter Trajectories

ROMS Daily Mean Surface Currents for

July 26, 2009

Observed Drifter Trajectories –

July 25 – 27, 2009

Sound Predictions 2009 Field

Experiment, Prince William Sound, Alaska

1

4

July 31 – Aug 3, 2009

July 20 – 26, 2009

July 27 – 30, 2009

ROMS vs. Assimilated DataHF Radar Surface Currents

1

5

ROMS vs. Assimilated Data: HF Radar Surface Currents

RM

S (

cm/s

)S

pati

al C

orre

lati

on

Num

ber

of O

bser

vati

ons

(Lig

ht B

lue

Bar

s)

Julian Day 2008 1

6

.

ROMS Ensemble Forecasting: Surface Current Speed Spreads (cm/s)

Hour 6 Hour 48

.

1

7

SVP 10m Drifter, Release time: July 25 th, 02 GMT, End time: July 31st, 02 GMT.

Ensemble of ROMS simulated drifter trajectories, release time and location corresponding to that of the SVP

drifter shown above.

1

8

ROMS Forecast Performance vs. Persistence(Spatial correlation / RMS Difference)

Forecast Field ROMS Single Fcst ROMS Ensemble Fcst Persistence

24 hr Sfc Currents 0.90 / 7.4 cm/s 0.91 / 7.2 cm/s 0.72 / 14.1 cm/s

48 hr Sfc Currents 0.80 / 10.6 cm/s 0.82 / 9.9 cm/s 0.57 / 17.2 cm/s

PWS

1

9

Subsurface Currents in the PWS

Julian Day 2009 2

0

California (3 km) and Southern California Bight (1 km) domains

2

1

ROMS Forecast Performance vs. Persistence(Spatial correlation / RMS Difference)

Forecast Field ROMS Single Fcst ROMS Ensemble Fcst Persistence

24 hr Sfc Currents 0.78 / 12 cm/s 0.66 / 18 cm/s

48 hr Sfc Currents 0.66 / 17 cm/s 0.46 / 23 cm/s

SCB

2

2

SCB ROMS Forecasts vs. HF radar observed

surface currents

2

3

Comparison of Glider-Derived Currents

(vertically integrated current)

Black: glider

Red: ROMS

2

4

RMS: 8.5 cm/s, Correlation: 0.46

Subsurface currents in California:ROMS CA-3km vs Independent Data: Vertically-averaged glider currents

2

5

2006 MB06/ASAP/AESOP

Baroclinic Tides

2

6

Summary1) A portable ROMS-based nowcast/forecast/hindcast nested modeling system

assimilating coastal HF radar surface current measurements, satellite SSTs and in-situ

glider/mooring data capable of routinely producing near real-time nowcasts every 6

hours and daily 2-3 day forecasts for coastal ocean regions at a horizontal resolution of

1km was presented.

2) A key component of the system is a multi-scale 3DVAR assimilation methodology

that incorporates spatially varying and scale-dependent error covariances, scale-

dependent dynamic balance constraints and can simultaneously assimilate all types of

ocean observations.

3) Surface circulation patterns are quantitatively reproduced (RMS differences of 5-15

cm/s). Subsurface currents are qualitatively reproduced.

4) One and two day forecasts of surface current forecasts more skillful than persistence

forecasts and clearly show the positive impact of the assimilation of HF radar surface

currents at the surface and below.

ourocean.jpl.nasa.gov/{PWS, CA, SCB}2

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All slides that follow are 'extras'

Depth-Integrated Baroclinic Tide Energy/Energy Flux (M2)

Barotropic Tide Energy Flux (M2)

3

0

Barotropic Tide Current (M2)

Data Assimilation to enable Forecasting& estimate Uncertainty

Time

Stat

e of

Oce

an (e

.g.,

T, S

, Cur

rent

)

T T+ 6 hours T+ 72 hours

Forecast

True Ocean

Observations

EnsembleForecast

Error

Nowcast

3

2

HF Radar Total vs Radial Current Data Assimilation

Total currents data assimilation

(circle)

1st Guess(blue)

Reanalysis(red)

Radial current data assimilation

(triangle)

Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for

the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090.

Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation scheme

for the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002,

doi:10.1029/2006JC004042.

Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation

scheme and its application to coastal oceans. Quart. J. Roy. Meteorol. Soc., submitted.

Global to Regional&

Physics to Biology

Multi-scale (or “nested”) ROMS modeling approach

has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by

satellites

35

Impact of HF Radar Current Data on

Nowcast/ForecastNowcast

RMSROMS w/oCurrent DA

ROMS withCurrent DA

48-hour forecast

Surface Current Forecasting in the PWS: Two case studies

Mean distance between ROMS nowcast and Observed drifter trajectories

Hours since Deployed

Dis

tanc

e (k

m)

Data Assimilation Formulation

J=1

2x x f T B 1 x x f 1

2Hx y T R 1 Hx y

prescribed B optimization algorithm

Variational methods (3Dvar/4Dvar):

Sequential methods (Kalman filter/smoother)

dynamically evolved B analytical solution

Distance Offshore (km)