A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller
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Transcript of A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller
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A Forecasting system for the Southern California Current
Emanuele Di LorenzoArthur Miller
Bruce Cornuelle
Scripps Institution of Oceanography, UCSD
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Forecast the mesoscale eddies
Understand the physics that control their generation and evolution
Assess the biological response
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Observational Dataset
SouthernCalifornia Coast
and Baja
Temperature, Salinity and Zooplankton
1949 – 2003 seasonal data
20 m vertical resolution, from 0– 500 m
70 - 80 km horizontal grid
CalCOFI historicalsampling grid
California Cooperative OceanicFisheries Investigation
Hydrography
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The Strategy
Initialize the model by assimilating the slowly evolving component of the eddy fieldwhich can potentially lead to forecast skill over a period of 2 months
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The Strategy
The Method
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dFG
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The Green’s Function Method
Initialize the model by assimilating the slowly evolving component of the eddy fieldwhich can potentially lead to forecast skill over a period of 2 months
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A typical sampling of a mesoscale eddy
E1
“SSH”
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AVVISO TOPEX/ERS
[m]
E1
E2SSH
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“SSH”
SSH
CalCOFICoastal observation
Data Assimilation
Ocean CirculationModel
Satellite Data
Independentverification
Assimilation of CalCOFI T,S
65% reduction in error variancerelative to the model initial guess!
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SeaWIFS [M N/m3]
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Chl-a
[M N/m3]
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Chl-a
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Chl-a
Ecosystem Model
Forecasting and Hindcasting Ocean Productivity
Independentverification
CalCOFI in Situ
REFERENCE:
Di Lorenzo, E., A. J. Miller, D. J. Neilson, B. D. Cornuelle, and J. R. Moisan, 2003: Modeling observed California Current mesoscale eddies and the ecosystem response. International Journal of Remote Sensing, in press.
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1 1ˆ ˆ[ ] [ ]T TJ m P m Gm d R Gm d
You need to know the physics that goes into the assimilation scheme
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E1
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SKILL associated with Persistence of Initial Condition
SSH Surface T T 150 mda
ys
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Data is collected over a 20 day period
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SKILL evolution when the true initial condition is replaced with a 20 day averagein a dynamical forecast
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How about the uncertainties in Forcing Functions?
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SKILL evolution with errors in Forcing
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SKILL evolution with errors in Forcing (and Open BC)
Surface T T 150 m
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SKILL evolution with errors in initial conditionJUNE
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Wavenumber Spectra
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Forecast the mesoscale eddies
Real time forecast of CalCOFI in April 2003SCCOOS nowcast-forecast with UCLA and JPL
Understand the physics that control their generation and evolution
Error CovariancesSeasonal dependence
Assess the biological response
In progress….
Concluding remarks:
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ΔρEkman Pumping Δρ increase
Δh westward propagation off the Bight
Wind
P. Conception
Shelf
(a)
Instability processeson continental slope
currents
(b)
(c)
April July
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Ocean Temperature
ZooplanktonLoge Tot. Vol.
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Anomalies
199019701950 200019801960
C
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Observationsalong the California Coast
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Assimilation Method
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E1
“SSH”
CalCOFICoastal observation
Assimilation of CalCOFI T,S
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E1
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SSH
CalCOFICoastal observation
Data Assimilation
Ocean CirculationModel
Assimilation of CalCOFI T,S
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What have we learned about the mesoscale dynamics?
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What have we learned about the mesoscale dynamics?
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The Green’s Function Method
Green’s Function
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1 1[ ( ) ] [ ( ) ]T TJ m P m model m obs R model m obs
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Changes in SKILL associated with errors in Forcing
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