C-MORE 4: Our view from the COOL room: Building technology & hopefully knowledge in the coastal ocean

8/6/2019 C-MORE 4: Our view from the COOL room: Building technology & hopefully knowledge in the coastal ocean

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Ou

Bu

kn

OscG

alofromMa

DelaLamo

GGoBe

Oliver

Welcome to my lab

Grand Challenges?

-Humans have already altered theEarth system

-Quantitative understanding of theEarth system will require the human

processes to be included

-The cost of integrated global systemwill require ocean observatory

networks need to be dual use to allow

for sustained supportFriday, July 1, 2011


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MVCO/CBLAST

S W 0 6

L a T T

E

E S P r e

S S O /

M A R C

O O SDelaware

N E N A

C u b a

t o G r

a n d B

a n k s

( n e s t e

d w i t h

i n H y

C O M

o r M e

r c a t o r

) M A

B - G o M

( H a t t e

r a s t o

H a l i f a

x )

Hudson River

Nested Ocean Models

+ =S4DVAR procedure

LagrangefunctionLagrangemultiplier

At extremaof , we require:

S4DVAR procedure:

( 1) C ho os e an(2) Integrate NLROMS and compute J

(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 of J

(6) Back to (2)untilconverged

( )

( )

1

1

0 ( ) 0

0 0

0 (0) (0) 0 &(0)

0 ( ) 0 . .( )

ii i

i

T T i

i im m mi

b

d L NLROMS

dt

d L ADROMS

dt L

coupling 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 ii

d L J dt =

( )= + # #* +, -0

xx N x F ( )i i t = 1F F

( )i

i t 2 1

x x

( ) ( )i it i t 2 2 1

L

(0) b=x x[0, ]t " 3[ , 0 ]t " 3 (0)

( )1 (0) (0)(0) b J #$ = # #

$B x x

x(0)x

Nested Models Data Assimilation 4-D Forecasts

+ =

3-D NowcastsRemote Sensing Robots

Friday, July 1, 2011


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Tropical Storms

Population

Critical

Habitat Climate Change

OceanCirculation

What are the drivers of variability in my laboratory?



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SST

CHL

CHL

Schofield et al 2002Glenn & Schofield 2003 Glenn & Schofield 2009



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Hypoxia& Anoxia

ScottGlenn

JosKo

RobertChant

JohnWilkin



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40N

74W75W

39N

Temperature oC19 20 21 22 24

July 6, 98 - AVHRR

FieldStation

LEO

74W75W

FieldStation

Chlor-a (mg/m 3).1 .3 .5 1 2 4

July 11, 98 - SeaWiFS

LEO

HistoricalHypoxia/Anoxia



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A)

1 m s -1

North

Barnegatdelta

Cape Maydelta

LEOdelta

Te

w i n d

D) 8/5/93CTD Transect

B)

C)

E)

F) CODAR & SST

Song et al(JGR) 2002Friday, July 1, 2011


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30 X 30 km LEO CPSEAn Integrated Observatory



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Air-Sea InteractionModel

ROMSOcean Model

(KPP and MY 2.5Turbulent Closure)

Bottom BoundaryLayer Model

OperationalLow-Res COAMPS

Atmospheric

Model

ExperimentalHigh-Res RAMS

Atmospheric

Model

Atmosphere/Ocean Physical/Biological Forecast Models

ROMsAssimilation

MODASAssimilation



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Month Long Experimental Effort

2001 Real-time Ensemble Forecasts



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July, 2001 18 19 20 21

2

4

6

8

10

12

D e p

t h ( m )

Thermistor 2

4

6

8

10

12

D e p

t h ( m )

July, 2001 18 19 20 21

HR COAMPS / ROMS

KPP

2

4

6

8

10

12

D e p

t h ( m )

July, 2001 18 19 20 21

MY2.5

-!In an observationally richenvironment, ensemble forecasts

can be compared to real-time datato assess which model is closer to reali

and try to understand why.



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Shipboard surveys



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N o r t h

S o u t h

1 5 m

6

NorthFluorometer

South, offshore flow

Adaptive Sampling of Resolved Scales- Shipboard & AUV surveys

NorthVelocity



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D e p

t h ( m )

1

12

6

600 30Time (hr)

A b s o r p

t i o n a t

4 4 0 n m

( m - 1 )

1.0

0

Tidal cycle Upwelling

Optical profiler deployed on LEO-15 guest port



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r 2 = .95

r 2 = .74

POC represents potentially182 mol oxygen/kgUpwelling can account

For spatially distributionof recurrent upwelling eddies



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Science focus Land-Ocean : How does the dynamicsoceanography influence the transport and transformat

particulate and dissolved matter in coastal buoyant

Geyer and Fo

Downwelling Upwellin

Southern flowingturbid plume

Eastern offshore fshallow turbid p



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Input of organic matter is pulsed to coastal system as floods and punctuatedtidal squirts. Example, a tidal bore as it flows past the R/V Cape Hatteras

Salinity



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Wind data from NOAA NDBC station at Ambrose Light

HF RADAR tracking and dye labeling of plume



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0

1 2 3 4 5

4/17/2005 4/18/2005 4/19/2005 Date

0 2 4 6 8

10 12 14

4/10/2005 4/11/2005 4/12/2005 4/13/2005 Date

P r o d

u c t i v i t y ( m g C / m ^ 3 / h r )

New Plume Old Plume

00.2

0.4

0.6

0.8

1

% c

a r b o n

f i x e

d

> 20 um 2 - 20 um

< 2 um

Stephanopyxis sp.

Thalassiosira

Lauderia

Skeletonema



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Ocean

Hudson Bay

Salinity

P e r c e n

t O x y g e n

S a

t u r a

t i o n

un

d e r

s a t ur

a t e d

s u p e r

s t a ur

a t e d

LATTE April 2005



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>20 m particulate trace metals and phosphorus - Ag, Al, Cr, Cu, Fe, P, Pb

s a

l i n

i t y

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:Hudson Shelf Valley


L TTE 2005 P t I j ti 2 Fi l hi b d


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LaTTE 2005 -Post Injection 2 Final shipboard surAfter luring the Cape Hatteras offshore.

The survey began on the HighwaWe were near the glider when itsurfaced. We saw currents rippingsouthward in a 10 m thick layer offreshwater along the highway --perhaps the most significantfreshwater transport we saw allweek.

Perhaps the most perplexinme is the Highway and why therehas been a lack of a strong coastalltrapped flow this week.

--- Bob Chant aboard the CapeHatteras, April 21, 2005



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Castaleo et al. 2008, 2010

MAdo

hydrogr

Temperextreme,

on

Salininsh



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EOF1: Winter (Nov-Feb)

EOF2: Spring (March-June)

EOF 1

EOF 2

a

c

Yi et



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january december

EOF2

january december

SSTPAR



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Water column

Sediment

NO 3 NH 4

Phytoplankton Zooplankton

Largedetritus Small

detritus

N 2NH 4 NO 3

Organic matter

Mineralization

Nitrification

D e n i t r i f i c a t i o n

M o r t a l i t y

Gr a zin g

Nitrification

Fennel et al., 2006

MN



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w

durs

ph

1975 2010Year

F i l t e r c o

l o r

Z o o pl ank t on

( A D C P

)



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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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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

Scientists nto t

Oscar treintegrasociety a

LATTE exp



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Science Community Workshop 1 5

Observatory (simulated) data

Virtual Ocean

Design, Testing and Deploy

Models

Data Assimilation

DataAnalysis

Science Questions & Drivers

~100 m

~3 kmSensor &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 in real-t

Community BlogData Portal



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Weathe



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5 differentsatellitesensors



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Model A Model B

Model CModel D

5 ocean numericalmodels run inforecast mode:

2 versions of ROMS2 versions of HOPs1 version of POM



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Ensemble

Model

SST Obs.

Scientists could compare observations (single platform or means) with models (individual or mea



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The same for in situ measurements


Model A Model B Model C Model D



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Discussion during experiment develops new tools during the experiment

Equal Weighting Objective Weighting



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Observation model comparisons spurred discussion on tools for synth

HF Radar Obs Ensemble Model



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Ensemble meanmodel

Variance com

Variance com



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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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Distributed decisionmaking

using live webservice tools



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Increase model resolution Reduce forecast error



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Op-Box determined by ASPEN

!MOOS-IvP autonomy on-boardeach IVER AUV

!Real-Time communicationbetween AUVs and shore usingacoustic modems

!Environmental sampling usingCTD

Arabella track

Hammerhead track

HammerheadMIT/NUWC

RemusRutgers

GreatwhiteCal. Poly

High resolutionunderwater planning

Smart robots

Distributedcontrol



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OthRutgers Web Site Statistics


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A v e r a g e

H o u r l y H

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General Public69%

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C-MORE 4: Our view from the COOL room: Building technology & hopefully knowledge in the coastal ocean