1 a three-dimensional variational data assimilation in support of coastal ocean observing systems...

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A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems

Zhijin Li and Yi ChaoJet Propulsion Laboratory

Jim McWilliams and Kayo Ide

UCLA

ROMS User Workshop, October 2, 2007, Los Angeles

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Integrated Ocean Observing System (IOOS):Modeling, data assimilation, forecasting and adaptive sampling

Theoretical Understanding &

NumericalModels

Products

Users:Managers

Education & Outreach

Observations(satellite, in situ)

Feedback & Adaptive Sampling

Information

Data Assimilation

Observing System Design

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Outline

1. Real-time Regional Ocean Modeling System (ROMS)

2. Three-dimensional variational data assimilation

3. Assimilated observations

4. Evaluation of analyses and forecasts

5. Observing system experiments (OSE)

6. Relocatability

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Coupled with Tides

Sea Surface M2 Tidal Currents

ROMS Simulation HF Radar Obs

RMS Error of SSHs

Tide Gauge

Jet Propulsion Laboratory

We couple ROMS with a tidal model. 1 tidal current crucial. foreaxample, trajectory related, rescue, oil spill, with and without tides, the trajectories are from different worlds. . 2 many observation can not be detided, 3 real-time as real time system, detiding leads a time delay.assimilation reuirement.

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Regional Ocean Modeling System

(ROMS): From Global to Regional/Coastal

12-km

Multi-scale (or “nested”) ROMS modeling

approach is developed in order to simulate the 3D ocean at the spatial scale (e.g., 1.5-km) measured by in situ and remote

sensors

1.5-km5-km15-kmModeling Approach


we use ROMS, with nesting capability. Two system levels: basin and regional system level. For example, EL NINO can impact almost everywhere. The El Nino's impact should be well represented.In this system, the pacific bbasin model was used to represent bainsacle phinomonon. the regional models are neted to this bain model. The region system has three nesting levels.We still prefer to on-line nesting. because of small time scales.

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Data Assimilation

Analysis

Forecast

Processing

Observations

When the numerical model is so good as its predictionis superior to the climatological (almanac) forecast

Model

Data Assimilation


Cycle. prerequiit is a good model. the model is not better than climate. no data assimilation

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ROMS Analysis and Forecast Cycle:Incremental 3DVAR

Aug.100Z

Time

Aug.118Z

Aug.112Z

Aug.106Z

Initialcondition

6-hour forecast

Aug.200Z

xa

xf

3-day forecast

y: observationx: model

6-hour assimilation cycle

)()(2

1)()(

2

1min 11 yHxRyHxxxBxxJ TfTf

x

xxx fa


following that cycle, we have this da and forecasting procedure.

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Why a There-Dimensional Variational Data Assimilation

• Real-time capability• Implementation with sophisticated and high

resolution model configurations• Flexibility to assimilate various observation

simultaneously• Development for more advanced scheme

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3DVAR: Weak Geostrophic Constraint and Hydrostatic Balance

TSfTS

aaTSfuv

aTSf

TS

uv

xx

xxx

xxx

x

x

x

S

T

v

u

x

aaTSuv xxx TS

Guv xx

aTS xxx

TSS xx

Geostrophic balance

Vertical integral of the hydrostatic equation

ax ageostrophic streamfunction and velocity potential

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Inhomogeneous and anisotropic 3D Global Error Covariance

Cross-shore and vertical section salinity correlation

SSH correlations

Kronecker Product

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Assimilated observations:Satellite infrared SSTs

NOAA GOES

NOAA AVHRR

Infrared, High resolutionCloud contamination

Microwave, Low resolution (25km)No cloud contamination

NASA AquaAMSR-E

NASA TRIMMTMI


Infrared and microwave STs are very complementary.

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Assimilated observations:Satellite SSHs along track

JASON-1

Resolution: 120km cross track, 6km along track


The bauty of SSH is that it can constraint Temperature/salinit vertical profile.

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Integrated Ocean Observing Systems

0

100

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Year Day

Nu

mb

er o

f C

asts

/Day

<55

<110

<220

<440

<1100

T/S profiles from gliders Ship CTD profiles Aircraft SSTs AUV sections

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Assimilated Current Observations

Acoustic Doppler Current Profiler (ADCP)

BottomShipboard Buoy

High Frequency Radar Mapped 2D surface current

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3DVAR with First Guess at Appropriate Time (FGAT)

fttt

tt

ttt

tftt

Ttt

ttt

tfttt

Tt

xxx

yHxxHRyHxxHxBxJ

000

0

0

0

0

0

000

2

1

2

1

12

1

2

1

1

2

1

2

1

3.5DVar

If ,0 tt xx FDAT 3DVar is equivalent to 4DVar


One question here. How to handle observation in the time window. Actually two issues here:Those observation came in different time;within the time window, we have multiple observation, such as HF radar messurements, mooring observation. In 3DVAR, they can not be fully solved. There are some helpful but only helpful methods. The most effective is FGAT.

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ROMS Performance Against Assimilated Data

August 2006 Mean

Temperature (C)

Salinity (PSU)

All Gliders Mean Diff RMS Diff

-0.3 0.3 0.0 0.75

-0.1 0.1 0.0 0.20


During AOSN, T/S analyses and forecasrts are good, but it is challenging for currents. The forecast skill is marginal for currents.

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Comparison of Glider-Derived Currents (vertically integrated current)

AOSN-II, August 2003 Black: SIO glider; Red: ROMS

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Forecast CorrelationPredictability during AOSN-II

Note: because gliders are moving, one cannot estimate the persistence

RMSE

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Observing System Experiment (OSE)

– Typically aimed at assessing the impact of a given existing data type on a system

– Using existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system

– Assessing the impact

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Observing System Experiment (OSE):Glider Data Denial Experiment

Temperature Salinity

1st week 2nd week

CalPoly SIO WHOI

w/o CalPoly glider

with CalPoly glider

RMS Error


When the system is at hand, we carried out a variety of OSEs. Here are examples.

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HF radar

ROMS without HF radar data assimilation

ROMS with HF radar data assimilation

Impact of HF Radar


OSE showed that T/S is not effective to correct the over-predicted velocities.This may point to the fact that it is necessary to assimilate velocity observations for a useful forecast of currents.

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Southern California Coastal Ocean Observing System (SCCOOS)

http://ourocean.jpl.nasa.gov/SCB

Southern California Bight

US WEST COAST

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Real-Time SCCOOS Data Assimilation and Forecasting System

http://ourocean.jpl.nasa.gov/SCB

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Evaluation with HF radar velocities

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Toward a Relocatable ROMS Forecasting System:Demonstration for Prince William Sound, Alaska

9-km

1-km3-km


This system has been running real-time for two 2 region. It is moving to Alaska. Next year this time, there will be a field rexperient to test and train this system, test its capability for a real-time system. Two major challenge: river charge, no good data; for DA, extreme inhomogeneous. We are working on incorporating extreme inhomogeneous error covariance.

1 a three-dimensional variational data assimilation in support of coastal ocean observing systems...

Documents

roms slide

dvar slide

impact slide

track slide

govscb slide

relocatability slide

system design slide

data assimilation observations