baptista, y. zhang, g. law, j. needoba, n. hyde, s. frolov, p. turner, m. wilkin, c. seaton,

Near real-time predictions of salinity intrusion in a river-dominated estuary: tales and implications of a challenging cruise

A. Baptista, Y. Zhang, G. Law,J. Needoba, N. Hyde, S. Frolov,P. Turner, M. Wilkin, C. Seaton,B. Howe, D. Hansen

Modified from a presentation to the Unstructured Grid Workshop, Halifax, Sep 2008

“CMOP: Transforming Ocean Exploration”

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Outline

The “mature” observatory The “inconvenient” cruise The short term “fix”

Open benchmark “retrospective “analysis

Jul 2008since 1996 Jul 2008

Aug-Sep 2008

Skill metrics

Sep 2008Sep 2008


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Conclusions

• The river-dominated CR river-to-ocean system provides major scientific and management challenges

• The end-to-end observatory SATURN offers a modern and comprehensive monitoring and modeling infrastructure

• Under-predicted SIL in a recent cruise has challenged the SATURN modeling skill, leading to a new benchmark

• SELFE has met most of the benchmark challenges through added resolution. But, will other codes do better?

• Allied with Opendap-CF standards, an Open CR benchmark could offer a stringent snapshot of modeling skill across leading-edge models, with automated updates

• The goal is to unite and cross-inform (not divide) the multiple unstructured-grid model communities

We invite broad participation!


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SATURN: an end-to-end observatory

Stakeholders

Cyber-infrastructure

Observation network

Modeling system


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• SATURN mobile platforms

• CMOP cruises

Observation network

• CORIE stations

• SATURN “endurance” stations

• SATURN “pioneer” stations

• Land-based remote sensing

• Context networks:

1 Slocum glider2 REMUS-100


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Circulation modeling system

Function• Support cruise planning,

execution and analysis• Characterize processes• Characterize long-term

variability• Characterize and

anticipate change• Re-design observation

network

Mechanisms• Daily forecasts (multiple)

• Multi-year simulation databases (multiple; since 1999)

• Scenario simulations– Climate– Human activities– Plate displacement

Redundancy (models/simulations) as philosophyCodes (past): QUODDY, ADCIRC, POM Codes (current): ELCIRC, SELFE


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What makes SELFE the current default model

Robustness

Ability to represent complex circulation processes and features, as required by CMOP research

Computational efficiency

MPI SELFE v2.0g Intel Xeon 2.3GHz cluster (canopus) with GBit connection ~27K horizontal nodes; 24 S levels; ~30m minimum equiv. diameter

with 30s step: ~9x faster than real time with 50s step: ~15x faster than real time

** See Joseph Zhang’s presentation, Friday afternoon **


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Blind retrospective cruise analysis – estuary

LMER - observations SELFE simulation

psu psu

… shows ability to represent complex and episodic features

June 1999

Sa

lin

ity

Cruise data courtesy D. Jay


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Blind retrospective cruise analysis – plume

Pt Sur path (surface )

Da

ta c

ou

rtes

y D

. Ja

y (R

ISE

pro

jec

t)

RMSE=2.64 psucorrelation = 0.80

● Cruise data

X SELFE simulation


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Coarse scale cruise planning/analysis

Maximum bottom salinity in the estuary over cruise period

Minimum surface salinity in the plume over cruise period

Total RNA content from the Aug 2007 CMOP cruise

Cru

ise

dat

a c

ou

rte

sy

L.

Her

fort

an

d M

. S

mit


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Forecast skill: prediction of plume location

Cru

ise

dat

a c

ou

rte

sy

B.

Cru

mp


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Goal: validate simulation of SIL (Salinity Intrusion Length)

SIL has a clear response to river discharge, and is being consider as a possible “sentinel” for CR variability and change


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SIL: difficult to measure …

(a) Data collected by David Jay on LMER and NOAA cruises

Ch

aw

la,

Ja

y,

Ba

pti

sta

, W

ilk

ina

nd

Se

ato

n,

CS

R 2

00

8


14

10:0909:00 09:32

… and difficult to simulate (forecast; fDB16; July 13)

07:23

Cru

ise

da

ta c

ou

rte

sy

J.

Ne

ed

ob

a

08:41


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Exploring options (in forecast mode, during the cruise)

Data assimilation (DA)Method of Frolov et al. 2008

• Model-independent• Reliant on fast model surrogates (SVD decomposition, machine-learning trained

Grid refinement• nchannel• schannel

…


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Grid refinement

fDB16

Refined grid(nchannel)

mottb cbnc3

grays


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fDB16

nchannel

July 170:30am

July 170:30am

Bottom salinity (forecasts; July 17)

July 17July 16

Tide (at grays )

1.6m

-1.5m

DA goes here

DA trained on DB16

July 170:30am


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CMOP July 2008 cruise: Real-time forecast

da

nchannelfDB16

July 17 200809:59

DA


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Salinity at challenging stations (forecasts, July 16-17)

nchannel da

fDB16 nchannel DA

nchannel DA

mottb

cbnc3

fDB16


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Retrospective analysis

Period Target features/processes Observations

SI-01 Sep16-Oct13 2004

Salinity intrusion: two consecutive spring-neap sequences show distinct salinity patterns at eliot (modest salt penetration in the first sequence, extensive in the second)

Extensive fixed-station data

SI-02 Jun-Jul2008

Salinity intrusion: 5-day with > half a tidal cycle each in one of the two channels; mostly flood spring tide.

High-quality CMOP cruise data

VS-01 Jun11-25 1999

Vertical salinity structure High-quality LMER profiling data

RV-01 Apr-Nov2002

Residual velocities and salinity structure. Not a big spring freshet year, however.

ADCP data at am012, am169, tansy, red26, and coaof, as well as fairly extensive S-T data (including 3 level at red26 abnd am169, some eliot, and some sveni with salt)


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Grid refinement

fDB16

eliot

Refined grid(“hires”)

# nodes: 27416# elements: 53314# levels 24min element area: 942 m^2max element area: 89834 m^2

grays


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“hires” hindcasts (eliot; Oct 2004)

hirest=30sec

DB16

hirest=30sec

DB16


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“hires” hindcasts (eliot; Oct 2004)

hirest=30sec

hirest=50sec

DB16

hirest=75sec


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Forecast (grays; Sep 15-16 2008)

RMSE= 5.2 psu

RMSE= 1.6 psu

Sal

inity

`fhires; t=20sec

fDB16


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Definition of Skill Assessment metrics

Name Definition Notes

Index of agreement

IOA ranges from 0 to 1 (1 is perfect skill; 0 is no skill)

Mean square error

MSE0 (0 is perfect skill)

Root mean square error

RMSE0 (0 is perfect skill)

Correlation skill score

Perfect skill: mo=1

Normalized standard deviation

Perfect skill: Nstdev=1

Model bias Perfect skill: MB=0

2

21 m o

m o o o

X XIOA

X X X X

22 m oRMSE Smo n

X X

,

( ) ( )

m omo

m o

Cov X X

Var X Var X

( ) ( )m o

O

X XMB

n

XXSMSE ommo

22 )(

2

2

)(

)(

oo

mm

X

XNSTDEV


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Forecast skill assessment (fhires; Sep 15-16, 2008)

Correlation skill IOA

RMSE N

Biofouled sensor

Biofouled sensor

Degraded sensor

Telemetry interrupts

Sta

tio

ns


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Hindcast skill assessment (sandi; salinity; IOA)

tide


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Hindcast skill assessment (sandi; salinity; correlation)

tideC

orre

latio

n sk

ill


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CR context and issues

Climate forcing• Pacific Decadal Oscillation & ENSO (precipitation, ocean climate)• Global climate change(sea level rise, snow pack)

Q (

m3/s

)

1997

2001

2002

W

E

S

N

Winter 01

E W

N

S

Summer 01

E

cou

rtesy

J. Barth

?

Barnes et al. 1972


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Selected E-GRs


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System response to forcing: estuary

am169

Sa

lin

ity

(p

su

)T

ide

ra

ng

e (

m)

Q (

m3/s

)

Salinity intrusion


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CR open benchmark

• Similar to NOAA’s Delaware Bay “model evaluation environment”, in that it enables cross-model comparisons

• Distinct in estuary type (river-dominated estuary) and philosophy• Enable continuous enhancement of multiple models and exploration of diverse

modeling strategies• Maximize value-added expertise of model developers/expert users, while

minimizing their time investment• Dynamic timeframes (blending controlled hindcasts with continuous blind

forecasts)• Focus on unstructured grid models

• Implementation phases– CMOP-driven SELFE pilot (on-going)– CMOP-assisted pilots for other lead models with by-invitation participation of the

respective developers / expert users (a ~12 month effort)– Open to community (early 2010) and consider exporting (2011)

• Enablers– CMOP’s SATURN modeling system & Rapid Deployment Forecasting System– OpenDAP-CF standards for unstructured grid models (synergistic effort led by Rich

Signell, with participation of at least the FVCOM, ADCIRC, SELFE, ELCIRC communities)


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Planning


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baptista, y. zhang, g. law, j. needoba, n. hyde, s. frolov, p. turner, m. wilkin, c. seaton,

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