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MIRAS performance based on OS data SMOS MIRAS IOP 6 th Review, ESAC – 17 June 2013

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

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MIRAS performance based on OS data SMOS MIRAS IOP 6 th Review, ESAC – 17 June 2013

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  1. 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

  2. MIRAS performance impact on OS data To review influence on OS retrieval of: • Anomaly issues • Operations • Changes in instrument configuration • Calibration issues: short and long-term drift effects SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 2

  3. MIRAS performance impact on OS data 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 3

  4. MIRAS performance impact on OS data 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) SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 4

  5. MIRAS performance impact on OS data • Anomaly issues • 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 ... SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 5

  6. MIRAS performance impact on OS data • 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) SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 6

  7. MIRAS performance impact on OS data • 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 7

  8. MIRAS performance impact on OS data • 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) SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 8

  9. MIRAS performance impact on OS data Orbital Drift 0.5 K Residual Tb driftsthatimpacton OS errors (1 K ≈ 2 psu) and shouldbecorrectedby OTT Seasonal Drift: 1 K Initial Transient 12 Month Drift 0.1 K/year J. Tenerelli, CLS

  10. MIRAS performance impact on OS data • OTT computation and ingestion has a strong impact on retrieved OS Reprocessed vs. operational SMOS-Argo: Same data but OTT bi-weekly and computed in themiddle of theperiod (repro) vs. OTT monthly and computed at thebeginning (oper) Thiseffectismuch more relevantthantheimpact of synchronisationbetween OTT and NIR calibration J. Martínez, SMOS-BEC SMOS MIRAS IOP REVIEW MEETING (05) ESAC, 09 Nov 2012 10

  11. MIRAS performance impact on OS data • Unrealistic salinity variability with monthly OTT Twomapsbuiltwith 9 days of SMOS L2OS products (asc + desc) separatedby 9 days: 24 May – 1 June 2012 11 – 19 June 2012 Usingthesame OTT We observe a global fresheningthat has no oceanographicmeaning in this time interval Instrumental orexternalgeophysicalvariability? If OTT correctionwas OK foronemap, certainlynotfortheotherone SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 11

  12. MIRAS performance impact on OS data Other details still under discussion SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 12

  13. MIRAS performance impact on OS data 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) SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 13

  14. MIRAS performance impact on OS data • Retrieved mean salinity drift during 2012 SSS1 – same results with SSS2/3, Same trend with ascending/descending, so not TEC, Sun or galaxy Seemingly related to instrument behaviour P. Spurgeon, ARGANS

  15. Relating SSS mapbiases to Hovmoller plots of biases J. Tenerelli, CLS Jun,Jul,Aug2012 Nov,Dec,Jan 2012/3

  16. SSS biastransformsinto first Stokes biasnearlylinearly J. Tenerelli, CLS Jun,Jul,Aug2012 Nov,Dec,Jan 2012/3

  17. SSS biastransformsinto first Stokes biasnearlylinearly J. Tenerelli, CLS Jun,Jul,Aug2012 Nov,Dec,Jan 2012/3

  18. AF-FOV bias trends: 40oS to 5oN Trends of one-slope and calibrated L1 solutions for AF-FOV meanbias in (Tx+Ty)/2 compared to trends in latitudinally-averaged Tp7 deviations. Tp7 curves are offset to fit on these figures. J. Tenerelli, CLS First Stokes biases in asc and desc passes exhibit a long-term trend thatis not present in Tp7. Also amplitude of bias drop late in year in descending passes isincreasingfromyear to yearwhile the variation of Tp7 is not. So perhaps a thermal effectis not the whole story; perhaps L-band brightness of sunplays a role.

  19. Comparison to NIR TA drift: 40oS to 5oN J. Tenerelli, CLS Overall, for ascending passes thereis good correspondencebetween NIR TA evolution (red and bluecurves) and AF-FOV biaswith and without direct sun correction (cyan and green curves).

  20. Latitudinal biases of Tvwind at 7m/s • 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. • Inaccuracies in modeling of Tbgal, Tbflat, Tbatm and Faraday rotation can not explain the latitudinal drifts in TBwind. • Empirical estimate of TBwind versus WS from SMOS TB is dependent on various seasons and on the TB versions. L1C v504 July TBwf X. Yin & J. Boutin, LOCEAN

  21. Latitudinal biases : seasonal behaviour Total drifts from 55S to 0S are close in July and August and are different to the value in December July Aug Dec. Orbital dynamics of Tp7 are close in July and August and are different to the value in December X. Yin & J. Boutin, LOCEAN

  22. MIRAS performance impact on OS data 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June 2013 22

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