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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
“CMOP: Transforming Ocean Exploration”
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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 **
“CMOP: Transforming Ocean Exploration”
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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
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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
“CMOP: Transforming Ocean Exploration”
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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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