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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.

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Presentation on theme: "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."— Presentation transcript:

1 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

2 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

3 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

4 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

5 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

6 Unexplained change in April 2010 Only data after Commissioning used for statistical OS analysis NIR_AB Tp7 jump on 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

7 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

8 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

9 Initial Transient 12 Month Drift 0.1 K/year Orbital Drift 0.5 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

10 SMOS MIRAS IOP REVIEW MEETING (05) ESAC, 09 Nov 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

11 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 – 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

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

13 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June

14 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

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

16 SSS bias transforms into first Stokes bias nearly linearly Jun,Jul,Aug 2012 Nov,Dec,Jan 2012/3 J. Tenerelli, CLS

17 Jun,Jul,Aug 2012 Nov,Dec,Jan 2012/3 SSS bias transforms into first Stokes bias nearly linearly J. Tenerelli, CLS

18 AF-FOV bias trends: 40 o S to 5 o N 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

19 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: 40 o S to 5 o N J. Tenerelli, CLS

20 Latitudinal biases of Tv wind 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.

21 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

22 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 SMOS MIRAS IOP REVIEW MEETING (06) ESAC, 17 June


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