C-MORE Schofield Lecture 1

7/31/2019 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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Sunday, July 1, 12

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


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


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


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


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


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


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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Sunday, July 1, 12

Shipboard surveys


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p y

Sunday, July 1, 12

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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Sunday, July 1, 12

)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)

Sunday, July 1, 12

Freshwater Plume Moves Out Across the Shelf


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Hudson Shelf Valley

Sunday, July 1, 12

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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Machines have improved

A technicians solutionin integrating the observatory

components

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O GO O



components

People need to sleepand are fragile

Humans become thebottle neck for

collecting data bytes

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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


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

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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Documents

C-MORE Schofield Lecture 1