SMOS QWG-6, ESRIN October 2011 OTT generation strategy and associated issues 1 The SMOS L2 OS Team

SMOS QWG-6, ESRIN October 2011 Bias between measured and modeled Tb seen early in 2010: –Spatial pattern persistent along and in different orbits –Similar using different ocean emissivity models –Error patterns have typical amplitude of ±5K –Foreseen before launch (Camps, 1998; Anterrieu, 2003) –… and mitigation techniques proposed (Camps et al., 2005; Meirold- Mautner et al., 2009) 2 Main problems in SSS retrieval

SMOS QWG-6, ESRIN October 2011 –Origin: intrinsic reconstruction errors: imperfectly known antenna patterns other potential reconstruction errors: Gibbs effect correction and FTT calibration residual errors –Removal/mitigation technique implemented in L2OS processor: Ocean Target Transformation (OTT) estimated as the mean misfit between data and each model over relatively homogeneous ocean areas (Tenerelli et al., 2010). –Such an estimation method further adjust the data to mimic the mean model behaviour: (mean angular dependence, mean contamination level)  reduce later salinity overall biases –The quality of the retrieved geophysical parameters strongly improves when the OTT is applied 3 Residual Tb bias after calibration

SMOS QWG-6, ESRIN October 2011 Problems –The systematic error pattern, as currently estimated from the misfit between data and model, significantly changes with dataset –Reasons for a time-varying bias pattern: Imperfections in instrument calibration, apparent drift Varying environmental conditions combined with imperfect forward models –Geophysical: dielectric, roughness, atmosphere –External sources: Sun, galaxy Number of snapshots used (noise reduction) 4 Time varying OTT

SMOS QWG-6, ESRIN October 2011 Reprocessing –Any new calibration (FTR, NIR) may sensibly modify the patterns Update OTT every 2 weeks (at new NIR calibration) Built from averaging orbits all along the 2 weeks –A constant OTT for 2 weeks will help account for slowly varying biases (instrumental drift, mean celestial contamination), but will not help in accounting for rapid changes (e.g. galactic plane) –Rules to select the data used for OTT computation will dictate the conditions of minimum biases in the latter retrieved salinities –Additive OTT: instrumental errors seem to be multiplicative in the AFFOV (EAFFOV not so simple) but model ones are not 5 OTT strategy for reprocessing

SMOS QWG-6, ESRIN October 2011 –Data selection criteria depend on reprocessing objective: Only optimal conditions and get the best salinity under these conditions, and then let the entire model errors propagate to the retrieved salinity when conditions are sub-optimal Increase the temporal consistency of the data (i.e. reduce the temporal variations of the overall salinity biases) by including the slowly-varying component of galactic modelling errors into the OTT –Need to establish criteria for data selection (use of L2OP flags, distance to galactic plane, adequate ocean region) –SMOS orchestration does not allow to apply a different OTT for ascending and descending half-orbits Critical: half the dataset will probably be strongly biased (either asc or desc depending on choice for OTT-asc or OTT-desc) To decide to use always ascending half-orbits or shift ascending/descending according to seasonal characteristics 6 OTT strategy for reprocessing

SMOS QWG-6, ESRIN October 2011 Ongoing studies for future improvements: –Separate instrumental and model systematic errors –For the instrumental ones: Estimation method independent of models Careful data selection to reduce environmental variability and reach acceptable pattern stability The resulting pattern can either be expressed under an additive or multiplicative formalism (e.g. multiplicative mask proposed by UPC)  evaluate the impact of each option –For the model ones: Work on their reduction through model improvement: galactic, Faraday, atmosphere Probably keep an additive formulation 7 OTT evolution