assessing the impact of ocean observing systems in support ...godae-data/op19/4.3.7-moore... ·...
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Assessing the Impact of Ocean Observing
Systems in Support of U.S. IOOS
Andy Moore1, Hernan Arango2, Chris Edwards1,Julia Levin2, Brian Powell3 & John Wilkin2
1: Dept. of Ocean Sciences, UC Santa Cruz2: Dept. of Marine and Coastal Sciences, Rutgers University
3: Dept. of Oceanography, University of Hawaii
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Assessing the Impact of Ocean Observing
Systems in Support of U.S. IOOS
Andy Moore1, Hernan Arango2, Chris Edwards1,Julia Levin2, Brian Powell3 & John Wilkin2
1: Dept. of Ocean Sciences, UC Santa Cruz2: Dept. of Marine and Coastal Sciences, Rutgers University
3: Dept. of Oceanography, University of Hawaii
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U.S. Integrated Ocean Observing System (IOOS)
AOOS
NANOOS
CeNCOOS
SCCOOS
GCOOS
CariCOOS
GLOS
MARACOOS
NERACOOS
SECOORA
PacIOOS
11 Regional Associations
The charge:• Observe• Analyze• Forecast• Products
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IOOS Stakeholders
Search & rescue
Fisheries
Water quality
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AOOS
CeNCOOS
SCCOOS
GCOOS
CariCOOS
GLOS
MARACOOS
NERACOOS
SECOORA
Ocean Observing Systems
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Remote Sensing
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AOOS
NANOOS
CeNCOOS
SCCOOS
GCOOS
CariCOOS
GLOS
MARACOOS
NERACOOS
SECOORA
PacIOOS
11 Regional Associations
MARACOOS : Mid-Atlantic Regional Association Coastal Ocean Observing SystemCeNCOOS: Central and Northern California Ocean Observing SystemPacIOOS: Pacific Islands Ocean Observing System
U.S. Integrated Ocean Observing System (IOOS)
Outline• Methodology• A MARACOOS
example• Summary
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A Typical Sequential Analysis-Forecast Procedure
4D-Var 4D-Var
Obs impact onforecast skill?
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Data Assimilation & Observation Impacts
xa
= xb+BGT GBGT +R( )
-1
y- H(xb)( )
analysis
background
backgrounderror cov
TL modelat obs pts
obserror cov
obs
obsoperator
( )I xScalar function: (e.g. transport, forecast skill,…)
Change due to 4D-Var: ( ) ( )I I I = −a bx x
Analysis equation:
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GBGT +R( )-1
GBMT ¶I ¶x( )xb
Observation Impact
Impact of observations on is given by:DI
Let’s look at what this really means…Langland and Baker (2004)
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Observation Impacts
Zonal shear flow
GBGT +R( )-1
GBMT ¶I ¶x( )xb
“Target” line of
delta functions
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Observation Impacts
Zonal shear flow
GBGT +R( )-1
GBMT ¶I ¶x( )xb
Adjoint Model
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Adjoint Model
Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
A weighted sum of Green’s
functions
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Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
A weighted sum of Green’s
functions
Covariance
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Covariance
Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
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Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
Tangent Linear Model
sampled at obs points
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Tangent Linear Model
sampled at obs points
Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
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Tangent Linear Model
sampled at obs points
Zonal shear flow
× Observations
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
××
×
×
altimeter track
mooring
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Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
Tangent Linear Model
sampled at obs points
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Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
Remove covariance between obs locations
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Zonal shear flow
Observation Impacts
GBGT +R( )-1
GBMT ¶I ¶x( )xb
Remove covariance between obs locations
Observation
impact
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(CGsolver)TGBMT ¶I ¶x( )xb
Zonal shear flow
Adjoint of CG solver
Observation Impact: Take 2
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Zonal shear flow
Observation Impact: Take 2
(CGsolver)TGBMT ¶I ¶x( )xb
Adjoint of CG solver
Observation
sensitivity
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Model surface and boundary forcing:Surface forcing derived from NAM [NCEP NOMADS]
USGS daily average flow [waterdata.USGS.gov]
Mercator open boundary conditions
Assimilation data sets: [real-time source]
Regional CODAR hourly [RU TDS]
IOOS glider T,S (1-hr delay) [RU ERDDAP]
AVHRR IR passes 6/day [MARACOOS TDS]
AMRS2+OceanSat mu-wave SST [NASA PODAAC]
Jason-2 & 3, CryoSat, AltiKa [RADS.tudelft.nl]
GTS XBT/CTD, Argo floats [OSMC NOAA ERDDAP]
Pioneer glider+mooring [RU ERDDAP]
Data assimilation system: ROMS ~7km, 40 levels4-dimensional variational (4D-var) data assimilationDual formulation (augmented RPCG)2 outer-loops, 7 inner-loops3 day assimilation windows
Regional Ocean Modeling System (ROMS): MARACOOS
Per 3 day cycle: SST ~105
HF radar ~104
in situ ~5X103
Altimetry ~103
Wilkin, Levin, & Arango
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A Typical Sequential Analysis-Forecast Procedure
4D-Var 4D-Var
I
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A Forecast Example
Impact Sensitivity
degrade
improve
I = change in mean-squared 3-day forecast error in surface velocity due to assimilating obsand evaluated at all HF radar obs locations.
Metric=MSE velocity
in situ T
in situ S
u & v
SST
SSH
in situ T
in situ S
u & v
SST
SSH
I
I = 1 N ui
f - ui
o( )2
+i=1
N
å vi
f - vi
o( )2
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• Some platforms appear to “borrow strength” from other platforms – “corroborating evidence”
• In the case of surface velocity obs from HF radar:- obs include Ekman and pressure driven flow, but only the
latter is “seen” by satellite remote sensing (but it’s presence is corroborated by u&v obs)
- most of the energy is in potential form -> T&S best (but circulation features corroborated by u&v obs)
• Observation impact and observation sensitivity provide important complementary quantitative information about the synergy between observations.
Summary
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I1= 1 t u
ndz ds
-h
0
òS
ò dt0
t
ò
Cross-shelf volume transport:
Cross-Shelf Exchange Circulation Metrics
200 m isobathtarget
Historical context:Garvine et al (1989)Linder and Gawarkiewicz (1998)Chen & He (2014)OOI Pioneer endurance array
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Obs Impact Obs Sensitivity
in situ T
in situ S
u & v
SST
SSH
RMS impact of SST observations on cross-shelf volume transport during 2017.
RMS impact on cross-shelf volume transport of excludingSST during 2017. in situ T
in situ S
u & v
SST
SSH
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log10
Sv
log10
Sv
rms contributionof SST to transport
rms change in transport is SST excluded
RMS Obs Impact RMS Obs Sensitivity