miras performance based on os data smos miras iop 6 th review, esac – 17 june 2013
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MIRAS performance based on OS data SMOS MIRAS IOP 6 th Review, ESAC – 17 June 2013. Prepared by: J. Font, SMOS Co-Lead Investigator, Ocean Salinity – ICM-CSIC. MIRAS performance impact on OS data. To review influence on OS retrieval of: Anomaly issues Operations - PowerPoint PPT PresentationTRANSCRIPT
MIRAS performance based on OS data
SMOS MIRAS IOP 6th Review, ESAC – 17 June 2013
Prepared by:
• J. Font, SMOS Co-Lead Investigator, Ocean Salinity – ICM-CSIC
To review influence on OS retrieval of:
a. Anomaly issues
b. Operations
c. Changes in instrument configuration
d. Calibration issues: short and long-term drift effects
MIRAS performance impact on OS data
SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 2
OS retrieval is difficult due to:– Low sensitivity of Tb to OS
– Unsolved interferometry issues
– Incomplete knowledge of geophysical model function
Consequences: – Higher impact of MIRAS performance on OS
than on SM
– L1 and L2 processors still not optimal
– Mission requirements only possible at L3
MIRAS performance impact on OS data
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Present situation:
– Imperfections in instrument calibration and image reconstruction are mitigated by L2OS processor before retrieval (OTT method), but significant biases remain uncorrected
– Difficult to separate impact of MIRAS performance from poorly corrected geophysical variability (Sun, galaxy, TEC)
MIRAS performance impact on OS data
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1. Anomaly issues
2. Operations
• Most of specific/time-limited problems or acquisition interruptions (anomalies, data loss or corruption) translate into flagging of deficient L1 or L2OS, so that are not used in building salinity maps (L3), e.g.
• Long gaps: “January failures” (arm A 2010, arm B 2011)
• Short gaps: unlocks, calibration events ...
MIRAS performance impact on OS data
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• Unexplained change in April 2010
• Only data after Commissioning used for statistical OS analysis
• NIR_AB Tp7 jump on 11.04.2012
• Not tracked on L2OS since it occurred during an orbit over land
• No impact observed on L3 maps (probably hidden by stronger variability sources)
MIRAS performance impact on OS data
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3. Changes in instrument configuration
• No impact of changes from redundant to nominal
• Dual/Full polarisation
• Require different OTT
• Slightly higher OS error in FP due to higher Tb noise, but more info on Faraday rotation
• Commissioning data not further used for long-term OS analysis
• LO calibration at different frequencies
• Used for specific impact studies on OS
• No evident effect observed on L3
MIRAS performance impact on OS data
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4. Calibration issues
• Short (orbital) and long-term drifts due to imperfect calibration
• Situation improving with improved antenna loss models, but still not optimal
• In L2OS v550 a time-varying asc/desc OTT was introduced to mitigate the impact of these drifts
• Monthly and not centred OTTs (DPGS) produce strong differences wrt bi-weekly and centred (reprocessing)
• New OTT strategy for v610 (OTT post-processor)
MIRAS performance impact on OS data
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Initial Transient
12 Month Drift
0.1 K/year
Orbital Drift0.5 K
Seasonal Drift: 1 K
J. Tenerelli, CLS
MIRAS performance impact on OS data
Residual Tb drifts that impact on OS errors (1 K ≈ 2 psu) and should be corrected by OTT
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• OTT computation and ingestion has a strong impact on retrieved OS
MIRAS performance impact on OS data
Reprocessed vs. operational SMOS-Argo:
Same data but OTT bi-weekly and computed in the middle of the period (repro) vs. OTT monthly and computed at the beginning (oper)
This effect is much more relevant than the impact of synchronisation between OTT and NIR calibration
J. Martínez, SMOS-BEC
• Unrealistic salinity variability with monthly OTT
MIRAS performance impact on OS data
Two maps built with 9 days of SMOS L2OS products (asc + desc) separated by 9 days:24 May – 1 June 201211 – 19 June 2012
Using the same OTT
We observe a global freshening that has no oceanographic meaning in this time interval
Instrumental or external geophysical variability?If OTT correction was OK for one map, certainly not for the other one
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MIRAS performance impact on OS data
Other details still under discussion
Seasonal effects
• Seasonal variability of geophysical conditions (Sun and galaxy position) requires time-varying OTT to cope for L1 (Sun removal) and L2 (galactic glint modelling) processors deficiencies
• But also some seasonal or long-term variability linked to instrument behaviour seems also to be present (see next slides)
MIRAS performance impact on OS data
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SSS1 – same results with SSS2/3,
Same trend with ascending/descending, so not TEC, Sun or galaxy
Seemingly related to instrument behaviour
• Retrieved mean salinity drift during 2012
MIRAS performance impact on OS data
P. Spurgeon, ARGANS
Relating SSS map biases to Hovmoller plots of biases
Jun,Jul,Aug 2012Jun,Jul,Aug 2012
Nov,Dec,Jan 2012/3Nov,Dec,Jan 2012/3
J. Tenerelli, CLS
SSS bias transforms into first Stokes bias nearly linearly
Jun,Jul,Aug 2012Jun,Jul,Aug 2012
Nov,Dec,Jan 2012/3Nov,Dec,Jan 2012/3
J. Tenerelli, CLS
Jun,Jul,Aug 2012Jun,Jul,Aug 2012
Nov,Dec,Jan 2012/3Nov,Dec,Jan 2012/3
SSS bias transforms into first Stokes bias nearly linearly
J. Tenerelli, CLS
AF-FOV bias trends: 40oS to 5oN
First Stokes biases in asc and desc passes exhibit a long-term trend that is not present in Tp7. Also amplitude of bias drop late in year in descending passes is increasing from year to year while the variation of Tp7 is not. So perhaps a thermal effect is not the whole story; perhaps L-band brightness of sun plays a role.
Trends of one-slope and calibrated L1 solutions for AF-FOV mean bias in (Tx+Ty)/2 compared to trends in latitudinally-averaged Tp7 deviations. Tp7 curves are offset to fit on these figures.
J. Tenerelli, CLS
Overall, for ascending passes there is good correspondence between NIR TA evolution (red and blue curves) and AF-FOV bias with and without direct sun correction (cyan and green curves).
Comparison to NIR TA drift: 40oS to 5oN
J. Tenerelli, CLS
Latitudinal biases of Tvwind at 7m/s
L1C v504
July
TBwf
X. Yin & J. Boutin, LOCEAN
1. Latitudinal drifts in TBwind deduced from SMOS TB of v3 and v5 are observed, especially at low incidence angles in EAFFOV and at large incidence angle above 50°in the front of the FOV.
2. Inaccuracies in modeling of Tbgal, Tbflat, Tbatm and Faraday rotation can not explain the latitudinal drifts in TBwind.
3. Empirical estimate of TBwind versus WS from SMOS TB is dependent on various seasons and on the TB versions.
July Aug Dec.
Total drifts from 55S to 0S are close in July and August and are different to the value in December
Orbital dynamics of Tp7 are close in July and August and are different to the value in December
Latitudinal biases : seasonal behaviour
X. Yin & J. Boutin, LOCEAN
Conclusions:– General: difficult to extract MIRAS performance
impact from OS products
– Anomalies and operation issues result in data loss or flagging, then not used for OS
– No impact of configuration changes
– Imperfect calibration mitigation addressed through OTT (only partly successful)
– Unexplained orbital and seasonal effects present
– Longer time-scale drifts observed, maybe linked to instrument behaviour
MIRAS performance impact on OS data
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