Data assimilation in RTOFS
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Data assimilation in Data assimilation in RTOFSRTOFS
Carlos LozanoCarlos Lozano
MMAB/EMC/NCEPMMAB/EMC/NCEP
April 23 2007April 23 2007
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SSTSST
Data: AVHRR, GOES and in situData: AVHRR, GOES and in situ– GOES bias is removedGOES bias is removed
Algorithm: Algorithm: – Two 2DVAR analyses: one for yesterday Two 2DVAR analyses: one for yesterday
and one for todayand one for today– Time interpolated temperature is nudge Time interpolated temperature is nudge
while integrating from yesterday to while integrating from yesterday to todaytoday
– Nudge is a pseudo heat flux source/sink Nudge is a pseudo heat flux source/sink at the model top layerat the model top layer
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SSHSSH
Data: JASON and GFOData: JASON and GFO– AVISO is not operationalAVISO is not operational
Algorithm: 2DVAR SSH, and 1D Algorithm: 2DVAR SSH, and 1D covariance in the vertical. covariance in the vertical. SSH=MDT+SSHA with MDT from Rio SSH=MDT+SSHA with MDT from Rio (2005).(2005).
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Sv
Results from operations as well as parallel runs give higher estimates of net transport for the section Florida to Bahamas as compared to the daily cable data. The parallel run has a similar variability to the operations but better estimates of mean transport.
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Low Frequency Boundary Low Frequency Boundary ConditionsConditions
Internal Mode:
a) Extrapolation of velocity fluxes for advection and momentum
b) Relaxation of Mass Fields T, S and P (interface thickness) in the buffer zones
Tk t+1 = Tk t + ∆ t μ ( θk
t - Tk t )
Sk t+1 = Sk t + ∆ t μ ( θk
t - Sk t )
Pk t+1 = Pk t + ∆ t μ ( θkt - Pk t )
where θ represents a slowly varying estimate (here, climatology), k is the layer and μ-1 is the relaxation time scale.
The width of buffer zones and values of μ-1 are defined a priori.
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Low Frequency Boundary Low Frequency Boundary ConditionsConditions
External Mode:
Normal transports and elevations determined from T,S climatology and Mean Dynamic Topography using: a) Thermal wind relations b) Absolute geostrophic velocity determined by either i) assuming a level of no motion, or ii) constrained by the slope of mean sea surface
elevation, MDT, from Maximenko, Niiler, McWilliams (GJR,2005).c) The mean of mean sea level is taken from MDT.
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Low Frequency Boundary ConditionsLow Frequency Boundary Conditions
Data assimilation modulates directly Data assimilation modulates directly the low frequency mass field and the low frequency mass field and sea surface height:sea surface height:
a)a) SST: mixed layerSST: mixed layer
b)b) SSH: water column and SSHSSH: water column and SSH
c)c) CTD: water column and SSHCTD: water column and SSH
But; there is no feedback…But; there is no feedback…
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Large scaleLarge scale
Large scales O(>500km) are well Large scales O(>500km) are well resolved by observationsresolved by observations
Internal dynamics at the mesoscale Internal dynamics at the mesoscale is influenced/modulated by the is influenced/modulated by the ambient potential vorticityambient potential vorticity
Are we introducing the large scales Are we introducing the large scales correctly in the assimilation?correctly in the assimilation?
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ExperimentExperiment
Three simulations (from Jan 1 to Three simulations (from Jan 1 to March 31 2007)March 31 2007)– Central: No assimilation (*)Central: No assimilation (*)– With SST assimilationWith SST assimilation– With SST & SSH assimilationWith SST & SSH assimilation
Model parameters and forcing nearly Model parameters and forcing nearly identical to those used in operationsidentical to those used in operations
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Gulf of MaineGulf of Maine
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RemarkRemark
The large scale estimates derived from the The large scale estimates derived from the model and from the combination of model model and from the combination of model and data assimilation require and data assimilation require improvements in some geographical areas improvements in some geographical areas (of practical interest). (of practical interest).
– Model Model – DataData– Data assimilation Data assimilation