C-MORE Schofield Lecture 1
Transcript of C-MORE Schofield Lecture 1
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Themes-Some of the new technologies your generation will have
-There are MANY UNKNOWN UNKNOWNS-Automation is coming
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GSFC, NASA
Color variability at multiple scalesaround Tasmania from CZCS
(winds? currents? bottom topography?)
Simulations of required numbemoorings to predict THE SIGN
cross shelf carbon transport
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Rumsfeld Unknown unknownsSunday, July 1, 12
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Scott
Glenn
Josh
Kohut
Oscar
Schofield
HOW WE ARE TRAINED TO GO TO SEA
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Ocean is hard to sample
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AutosubSouthampton Oceanography Center UK
HuginKongsberg Simrad, Norway
Martin-600
Maridan, Denmark
Explorer family,
ISE research, Canada
Odyssey,
Bluefin Robotics,
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Slocum gliders
Seaglider
Spray
C-Scout
Mauve
Gavia
REMUS
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Longitude(~2km)
Dep
th(m)
0
15
1E10 biolumi
3E10 biolumi
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Longitude(~2km)
Dep
th(m)
0
15
1E10 biolumi
3E10 biolumi
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Longitude(~2km)
Dep
th(m)
0
15
1E10 biolumi
3E10 biolumi
Depth(m)
Latitude(~300m)
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MVCO/CBLAST
SW06
LaTT
E
ESPr
eSSO
/MAR
COOSDelaware
NENA
Cuba
toGr
andB
anks
(nes
tedw
ithin
HyCO
M
orMe
rcat
or)
MAB-
GoM
(Hatt
eras
toHa
lifax
)
Hudson River
Nested Ocean Models
+ =
S4DVAR procedure
Lagrangefunction
Lagrangemultiplier
At extremaof , we require:
S4DVAR procedure:
(1) C ho os e an
(2) Integrate NLROMS and computeJ
(3) Integrate ADROMS to get
(4) C om pu te
(5) Use a descent algorithm to determine a down gradientcorrection to that will yield asmallervalue ofJ
(6) Back to (2)untilconverged
( )
( )
1
1
0 ( ) 0
0 0
0 (0) (0) 0 &(0)
0 ( ) 0 . .( )
ii i
i
T
Tii im m m
i
b
dLNLROMS
dt
dLADROMS
dt
Lcoupling of NL ADROMS
Lic of ADROMS
= =
= =
= =
= =
xN x F
N H O Hx y
x x
B x x x
x
1
( ) ( )N
T ii i i
i
dL J
dt=
= +
xx N x F ( )i i t= F F
( )i i t x x
( ) ( )i it i t
L
(0) b=x x
[0, ]t [ ,0]t (0)
( )1 (0) (0)(0)
b
J =
B x x x
(0)x
Nested Models Data Assimilation 4-D Forecasts
+ =
3-D NowcastsRemote SensingRobots
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Sargasso Sea
Front Sargasso SeaFront SaFrCold Core
Eddy Cold CoreEddyCold Core
EddyWarm Core
Remnant Warm CoreRemnant WRe
Lets say you are hunting whales
Knowledge of the environment will give you a tactical advantageKnowledge of future environment will give you a bigger tactical advanta
No in situ datainto the model
BSP in situ datainto the model
Gliders (4)into the
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Contributed Assets:
HF Radar NetworksUSF, USM
GlidersiRobot, Mote, Rutgers,
SIO, UDel, USF, Navy
Drifters & ProfilersHorizon Marine, Navy
Satellite ImageryCSTARS, UDel
Ocean ForecastsNavy, NCSU
Data/Web Services
ASA, Rutgers, SIO
USM HFR validation of SABGOM Forecastin region with satellite detected oil slicks
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Hurricane IreneAugust 2011
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Get offthe
damnbeach!!
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T Glid
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Two Gliders
Deployed by MARACOOS
in Hurricane Irene
RU16Deployed for EPA.Map bottom dissolvedoxygen.
Provided data onmixing during storm.
RU23Deployed for MARAMap subsurface T/Sstructure for fisheries
Damaged early - drRecovered by fisherProvided data on inecurrents during storm
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salinity
% oxygen
temperature55
55
0
0
55
depth
depth
depth
8/12 date
0
Hurricane IrHurricane Irene
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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CDOM
Backscatter
Ed491 (nm) 20
10
0
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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weather
mixingactual
water column
physicsMean
water column
hydrography
2nd and highertrophic levels
phytoplankton
OPTICAL
PROPERTIES
sediment
CDOM
Detritus
light
nutrients
rivers
In situ
Acoustic
Field
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SST
CHL
CHL
Schofield et al 2002Glenn & Schofield 2003 Glenn & Schofield 2009
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Hypoxia& Anoxia
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Hypoxia& Anoxia
ScottGlenn
JoKo
RobertChant
JohnWilkin
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July 6, 98 - AVHRR July 11, 98 - SeaWiFS
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40N
74W75W
39N
Temperature oC
19 20 21 22 24
y ,
Field
Station
LEO
74W75W
Field
Station
Chlor-a (mg/m3)
.1 .3 .5 1 2 4
y ,
LEO
Historical
Hypoxia/Anoxia
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A)
1 m s-1
North
Barnegat
deltaCape May
delta
LEO
delta
T
wind
D) 8/5/93CTD Transect
B)
C)
E)
F) CODAR & SS
Song et al(JGR) 2002
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30 X 30 km LEO CPSE
An Integrated ObservatorySunday, July 1, 12
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Month Long Experimental Effort
2001 Real-time Ensemb
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July, 200118 19 20 21
2
4
6
8
10
12
Depth(m)
Thermistor2
4
6
8
10
12
Depth(m)
July, 200118 19 20 21
HR COAMPS / ROMS
KPP
2
4
6
8
10
12
Depth(m)
July, 200118 19 20 21
MY2.5
-In an observationally richenvironment, ensemble forecas
can be compared to real-time da
to assess which model is closer to r
and try to understand why.
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Shipboard surveys
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p y
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Adaptive Sampling of Resolved Scales Shipboard & AUV surveys
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North
South
15m
6
North
Fluorometer
South, offshore flow
Adaptive Sampling of Resolved Scales- Shipboard & AUV surveys
North
Velocity
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Optical profiler deployed on LEO-15 guest port
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Depth(m)
1
12
6
600 30
Time (hr)
Absorptiona
t
440nm(
m-1
)
1.0
0
Tidal cycle Upwelling
p p p y g p
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Aug 5, 2012Sunday, July 1, 12
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r2 = .95
r2 = .74
POC represents potentially
182 mol oxygen/kgUpwelling can account
For spatially distribution
of recurrent upwelling eddies
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Science focus Land-Ocean: How does the dynamoceanography influence the transport and trans
particulate and dissolved matter in coastal b
Geyer and
Downwelling Upwelli
Southern flowing
turbid plume
Eastern offshore
shallow turbid
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Input of organic matter is pulsed to coastal system as floods and punctuat
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tidal squirts. Example, a tidal bore as it flows past the R/V Cape Hatter
Salinity
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
g y g
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
Sunday, July 1, 12
HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
Sunday, July 1, 12
HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
Sunday, July 1, 12
HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
Sunday, July 1, 12
HF RADAR tracking and dye labeling of plume
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Wind data from NOAA NDBC station at Ambrose Light
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)New Plume Old Plume
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0
1
2
3
4
5
4/17/2005 4/18/2005 4/19/2005Date
0
2468
101214
4/10/20054/11/20054/12/20054/13/2005
Date
Pr
oductivity
(mgC/m^3/hr) Old Plume
0
0.2
0.4
0.6
0.8
1
%
carbonfixed
> 20 um
2 - 20 um
< 2 um
Stephanopyxis sp.
Thalassiosira
Lauderia
Skeletonema
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Ocean
Hudson Bay
Salinity
Perce
ntOxygenSatu
ration
undersaturated
superstaurated
LATTE April 2005
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>20 m particulate trace metals and phosphorus - Ag, Al, Cr, Cu, Fe, P, Pb
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salinity
50 ng L-1
(Al, Fe, P g L-1;
Ag x 10, Al x 5, P x 10)
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Freshwater Plume Moves Out Across the Shelf
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Hudson Shelf Valley
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LaTTE 2005 -Post Injection 2 Final shipboaf l h ff h
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After luring the Cape Hatteras offshore.
The survey began on the HigWe were near the glider when
surfaced. We saw currents rsouthward in a 10 m thick layefreshwater along the highway perhaps the most significantfreshwater transport we saw aweek.
Perhaps the most perpleme is the Highway and why thhas been a lack of a strong cotrapped flow this week.
--- Bob Chant aboard the CaHatteras, April 21, 2005
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Castaleo et al . 2008, 2010
MAdo
hydrog
Tempeextreme
on
Salininsh
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january december
EOF2
january december
SSTPAR
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EOF1: Winter (Nov-Feb)
EOF2: Spring (March-June)
EOF 1
EOF 2
a
c
Yi et
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M
aximum
winterc
hlorophyll
concent ration
Percent of sto
5
020
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Yi et al su
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WHERE DO WE GO FROM HERE?
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WHERE DO WE GO FROM HERE?
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WHERE DO WE GO FROM HERE?
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WHERE DO WE GO FROM HERE?
Machines have improved
A technicians solutionin integrating the observatory
components
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WHERE DO WE GO FROM HERE?
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O GO O
Machines have improved
A technicians solutionin integrating the observatory
components
People need to sleepand are fragile
Humans become thebottle neck for
collecting data bytes
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WHERE DO WE GO FROM HERE?
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Machines have improved
A technicians solutionin integrating the observatory
components
People need to sleepand are fragile
Humans become thebottle neck for
collecting data bytes
Scientiststo t
Oscar reinteg
society aftexper
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Science Community Workshop 1 5
Observatory (simulated) data
Virtual Ocean
Design, Testing and Deploy
Models
Data Assimilatio
Data
Analysis
Science Questions & Drivers
~100 m
~3 km
Sensor &
Platform
Data Synthesis: Nowcast & Data Impact
Idea of Test(May 2009)
Virtual Test(Sep 2009)
Wet Test(Nov 2009)
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Scientists were distributed throughout the country & interacted i
Community Blog
Data Portal
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Weath
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5 differentsatellitesensors
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Model A Model B
Model CModel D
5 ocean numericalmodels run in
forecast mode:
2 versions of ROMS2 versions of HOPs
1 version of POM
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EnsembleModel
SST Obs.
Scientists could compare observations (single platform or means) with models (individua
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The same for in situ measurements
Science Community Workshop 1 13
Model A Model B Model C Model D
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Ensemble meanmodel
Variancecom
Variancecom
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Known constraints (slow0.5 knot, Battery, shippinglanes)
Uncertain constraints (time-varying 3D currents)
Operate autonomously &re-plan daily
From A to B in theshortest time
Follow a time-varyingfeature (shelf-slopesalinity intrusion)
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Scientificcommunity
Maropera
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Distributed decisionmaking
using live webservice tools
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Increase model resolution Reduce forecast error
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Science Community Workshop 1 2626
Science
Agents
Science Event Manager
Processes alerts andPrioritizes response observations
ASPEN
Schedules observations on EO-1
EO-1 Flight Dynamics
Tracks, orbit, overflights,
momentum management
Science
Alerts
Observation
Requests
Updates to
onboard plan
Science
Campaigns
Scientists
Hyperion
on EO-1
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Hyperion on EO-1
7.5 km by 100 km(30 m resolution)
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