The impact of the assimilation of SLA along track Observation with high-frequency signal in IBI system
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1 The impact of the assimilation of SLA along track Observation with high-frequency signal in IBI system M. Benkiran, C.Dufau (CLS) and Mercator Ocean Team
2 Context Models assimilation Altimetry Large mesoscale resolving, only forced by wind Nearly consistent Tuned for altimeter gridded products construction: Corrected from different signals, filtered, subsampled
3 Context Models assimilation Altimetry Large mesoscale resolving, only forced by wind Nearly consistent Tuned for altimeter gridded products construction: Corrected from different signals, filtered, subsampled regional, small scale resolving, forced by wind, pressure, tides,
4 Context Models assimilation Altimetry Large mesoscale resolving, only forced by wind Nearly consistent Tuned for altimeter gridded products construction: Corrected from different signals, filtered, subsampled regional, small scale resolving, forced by wind, pressure, tides, Consistent Tuned for assimilation in models
5 The TAPAS initiative TAPAS : Tailored Altimetric Products for Assimilation Systems OBJECTIVE : Define an altimeter along-track product that fit the assimilation needs: spatial resolution physical content Convential SLA Observations (e.g DUACS): VFEC : Verified, Filtered, SubSampled and Corrected) SLA vfec SLASat DAC LWE TIDE TAPAS SLA Observations, verified and Corrected SLA vxxc SLASat DAC LWE TIDE No filtering, no subsampling; DAC, LWE & TIDE are also provided to let to the user the possibility to use them or not Residual orbit ER, IB ER, Residual Tide Corr and Aliased HF signals
6 The TAPAS initiative TAPAS : Tailored Altimetric Products for Assimilation Systems TAPAS SLA Observations, verified and Corrected SLA vxxc SLASat DAC LWE TIDE No filtering, no subsampling; DAC, LWE & TIDE are also provided to let to the user the possibility to use them or not TAPAS products are used to assess the assimilation of altimetry data containing high frequency signals in a regional model (IBI12). Non along-track filtered Non sub-sampled (i.e. 7km instead of 21 Km) Without Dynamical Atmospheric Correction (DAC= IB LF + MOG2D HF )
7 IBI-1/12 SYSTEM (IBI : Iberian-Biscay-Irish area) Model(NEMO2.3, Madec et al. 1998, Madec,28): explicit free surface, «time splitting» + Variable volume formulation k-ε : the Generic Length Scale (GLS) formulation (Umlauf and Burchard, 23) Tides (including potential) : M2, S2, K2, N2, K1, O1, P1, Q1, M4,Mf, Mm Atmospheric pressure forcing Open boundaries from GLORYS2V1 ¼ reanalysis (daily) Data Assimilation: Reduced order Kalman Filter (SEEK formulation) 3D-VAR Bias corrections : for T and S Incremental Analysis Updates (IAU) : Analysis J-2.5 SST Correction in Bulk Quality Control of in situ observation Assimilated Observations: Along track SLA observations From AVISO, usual corrections applied filtering of tides in the model Background In situ profiles T & S from CORA3.1 data base Reynolds AVHRR ¼ SST
8 Objective : Assimilation of SLA along track Observation with highfrequency signal in regional model Experience Atm. Pressure forcing in Model High-frequency signal in Observation Period CONTROL Yes No 29 HF_ASSIM Yes Yes 29
9 Assimilation of SLA with the high-frequency signal (DAC = IB+MOD2G ) Model: Assimilation: Control Experience U 1... p... t A OpenBoundaries : ssh ssh SSH IB SLA Background SSH SSH TIDE SSH IB SSH LS _ Bar 1 SSH IB ( P ): InverseBarometer g pa SSH LS_bar : Large Scale of thebartropicheight computer by themodel p : Atmospheri c pressure(from ECMWF model), : thereference density A P : Spatial Averge(over the ocean) TAPAS SLA Observations, verified and Corrected MDT SLA vxxc SLASat DAC LWE TIDE DAC IBLF MOG2DHF LWE(Long Wave Error) : Residual orbit ER, IB ER, Residual Tide Corr and Aliased HF signals
10 Assimilation of SLA with the high-frequency signal (no more DAC = IB+MOD2G ) Model: Assimilation: HF_Assim Experience U 1... p... t A OpenBoundaries : ssh ssh SSH IB SLA Background SSH SSH TIDE SSH IB SSH LS _ Bar 1 SSH IB ( P ): InverseBarometer g pa SSH LS_bar : Large Scale of thebartropicheight computer by themodel p : Atmospheri c pressure(from ECMWF model), : thereference density A P : Spatial Averge(over the ocean) TAPAS SLA Observations, verified and Corrected MDT SLA vxxc SLASat DAC LWE TIDE DAC IBLF MOG2DHF LWE(Long Wave Error) : Residual orbit ER, IB ER, Residual Tide Corr and Aliased HF signals
11 Residu in m Impact of the assimilation of HF (SLA): Control vs HF_Assim (no DAC) Misfit = Data - Model forecast Residu in m Cloud Dispersion Residual vs Misfit in SLA on 29 (Control) Cloud Dispersion Residual vs Misfit in SLA on 29 (HF_Assim) Misfit in m SLA : J2 + J1n + En Slope : 7% Too large Misfit in m Slope : 41%
12 Residu in m Impact of the assimilation of HF (SLA): Control vs HF_Assim (no DAC) Misfit = Data - Model forecast Residu in m Cloud Dispersion Residual vs Misfit in SLA on 29 (Control) Cloud Dispersion Residual vs Misfit in SLA on 29 (HF_Assim) More efficient with High Frequency signal: More dispersed cloud Better Slope Misfit in m SLA : J2 + J1n + En Slope : 7% Too large Misfit in m Slope : 41%
13 Impact of the assimilation of HF (SLA): Control vs HF_Assim (No DAC) Misfit = Data - Model forecast Rms of Residu SLA : With and Without HF signal CONTROL : Solid line HF_Assim(no DAC) : Dashed line Diff Rms of Residu (HF_Assim Control) winter period winter period 4,17 4,25 Blue : improvement Red : degradation
14 Impact of the assimilation of HF (SLA): Control vs HF_Assim (No DAC) Misfit = Data - Model forecast Rms of Residu SLA : With and Without HF signal CONTROL : Solid line HF_Assim(no DAC) : Dashed line Diff Rms of Residu (HF_Assim Control) winter period winter period 4,17 4,25 Domain / Experiment IBI Rms(residual) Cm Shallow Water Rms(residual) Cm Deep Ocean Rms(residual) Cm CONTROL Blue : improvement Red : degradation -3.5% +3,3% -6,5% HF_Assim
15 Impact of the assimilation of HF (SLA): Control vs HF_Assim (no DAC) More Eddy kinetic energy Eke Near Surface (Control) in 29 Eke Near Surface (HF_Assim) in 29 Diff Eke (HF_Assim Control)
16 Impact on insitu data (Temperature& Salinity) Mean & Rms (Misfit) Misfit = Data - Model forecast Temperature (over all domain) Mean misfit (Control ) Mean misfit (HF_Assim ) Rms misfit (Control ) Rms misfit (HF_Assim ) Temperature Mean misfit ( Control ) Salinity (over all domain) Mean misfit ( HF_Assim) Rms misfit ( Control ) Rms misfit ( HF_Assim )
17 Impact on insitu data (Temperature& Salinity) Mean & Rms (Misfit) Misfit = Data - Model forecast Temperature (over all domain) Mean misfit (Control ) Mean misfit (HF_Assim ) Rms misfit (Control ) Temperature : No improvement for Temperature Mean and Rms (over all domain) -1 But no degradation Rms misfit (HF_Assim ) Salinity. 1. : A improvement Salinity (over in both all domain) Mean and Mean misfit ( Control ) Mean misfit ( HF_Assim) Rms misfit ( Control ) Rms misfit ( HF_Assim ) Rms especially near the surface(-1m)
18 Conclusions Improvement of DA diagnostic without the correction of the DAC in the model and data (SLA). More energy even at small scales, Salinity improvement -1 m. The physical content of the altimeter measurements assimilated need to be consistent with the physical content of the model Models need a specific altimeter product in order to reduce they errors
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