SMOS QWG-5, 30 May- 1 June 2011, ESRIN Ocean Salinity 1 1.Commissioning reprocessing analysis 2.New processor version: improvements and problems detected/solved 3.Present performance 4.Future evolution: ongoing studies

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Land sea contamination correction J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN URL:

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 3 Land contamination  Impact of correction implemented by Deimos on the strong halo around continental surfaces  to avoid multiplying the first Fourier parameter by the element of area (sqrt(3) * Distance_ratio * Distance_ratio/2)  L1PP run at BEC without and with correction  71 ascending orbits, 71 descending from August 2010  Tb at 42.5º; filtering 40 < Tb < 200  Tb maps: average per ISEA GP and then average for 1º*r*cos(lat).  SSS semi-orbits (problem in running several orbits at a time)

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Tb ascending maps

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Tb descending maps

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 6 Impact on SSS  SSS 3 semi-orbits  Run with patched L1PP and L2OS 3.17  Specific OTT computed from uncorrected and corrected L1

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 7 Uncorrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 8 Corrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 9 Uncorrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 10 Corrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Conclusion  The correction has removed the first order problem (strongest signal)  Back to the original scene dependant bias issue (A. Camps 2005)? 11

SMOS QWG-5, 30 May – 1 June 2011, ESRIN  Pre-launch semi-empirical roughness model (SSS3) was derived from data obtained during the WISE experiments ( ) on an oil platform in the NW Mediterranean  New fitting using actual SMOS data (residual after removing the rest of modelled emission components)  Guimbard et al., “SMOS semi-empirical ocean forward model adjustment” submitted to TGRS SMOS special issue 12 New semi-empirical roughness model

SMOS QWG-5, 30 May – 1 June 2011, ESRIN New semi-empirical roughness model 13

QWG-5, Frascati, May st, 2011 OTT sensitivity study J. Gourrion, M. Portabella, R. Sabia, S.Guimbard SMOS-BEC, ICM/CSIC

QWG-5, Frascati, May st, 2011 OTT sensitivity  DPGS OTT  Impact on OTT quality of different factors: 1.Number of snapshots used 2.Temporal variability and apparent drift 3.Latitudinal variability  Alternative OTT estimation strategy  Method and preliminary results

QWG-5, Frascati, May st, 2011  For a 16-days period dataset (Aug. 3 rd – Aug 18 th ), about snapshots are available after comprehensive filtering (land, outliers, descending overpasses)  N OTTs are computed by randomly selecting n snapshots among all available. (N-1) rms difference of the N OTTs are then computed.  N decreases with increasing n, leading to N=2 when n=6000, i.e., about half of the total amount in the 16-days period.  For consistency, the same experiment is repeated for two additional 16-days periods (Aug. 19 th – Sep 3 rd, Sep. 4 th – Sep 19 th ). The overall rms values are obtained by averaging the 3 16-day period scores.  As expected, OTT robustness depends on number of snapshots used. Current operational OTT has a 0.25K error only due to sampling. OTT sensitivity Impact of number of snapshots

QWG-5, Frascati, May st, 2011 OTT sensitivity Temporal variability  A 48-days period dataset (August-Sept 2010) is used and split into 8-days subsets. Same filtering than previous experiment.  The reference situation is given by the first 8-days subset.  For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n =  The OTT rms increase (relative to reference) indicates an increasing data inconsistency with time, i.e., apparent drift.

QWG-5, Frascati, May st, 2011 Ocean/ice transition Salinity ? Rain ? Roughness residuals ? New model 3 SSA/SPM model OTT sensitivity Latitudinal variability  A 16-day period dataset (Aug. 3 rd – Aug 18 th ) is used and split into 6° latitudinal band subsets.  The reference situation is given by the [36° S, 30° S] latitudinal band subset.  For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 610.  The OTT rms differences (relative to reference) mainly indicate potential forward model and auxiliary data errors.

QWG-5, Frascati, May st, 2011 OTT sensitivity OTT as mean departure from full forward model: summary  OTT robustness significantly depends on sampling. Current OTT computation should use a larger number of snapshots.  Temporal inconsistencies due to non-modelled instrumental/reconstruction instability and imperfect Foreign Sources modelling  Latitudinal inconsistencies due to imperfect modelling or auxiliary parameters  OTTs estimated from different datasets will vary depending on the distribution of sampled geophysical conditions  With current OTT methodology, the data are adjusted to reproduce the mean forward model behaviour (e.g., angular dependency): updated forward models are NOT independent from pre-launch versions (used to compute the OTT)

QWG-5, Frascati, May st, 2011 OTT sensitivity New and prelaunch forward models have similar angular dependence

QWG-5, Frascati, May st, 2011 OTT sensitivity  Objective: Estimate systematic errors in the antenna frame while avoiding use of forward models as much as possible  Main differences with current OTT:  do not use forward models  do not assume that geophysical variability is negligible BUT  select specific environmental conditions (U,SST,SSS,low galactic,…)  MEAN angular dependency is fitted with a simple polynomial function and removed from the mean scene to obtain the systematic error pattern  Work in progress: only five days of data processed in this study. New OTT estimation method: basics (1)

QWG-5, Frascati, May st, 2011 OTT sensitivity  Data are averaged over a large number of epochs: local geophysical anomalies are spread over the whole average image. They contribute more to systematic angular (varying with incidence angle) biases and less to residual variability WHILE OTT estimation method accounts for systematic angular biases  Auxiliary information only used for data selection before OTT computation (except for Faraday rotation effects)  X/Y data are rotated to obtain H/V scenes, while H/V OTTs are rotated back to get X/Y OTTs New OTT estimation method: basics (2)

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: comparison INCONSISTENT ANGULAR DEPENDENCE BETWEEN SMOS DATA AND PRE-LAUNCH FORWARD MODELS

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: stability (1) Selecting different wind speed conditions RMS VALUES CONSISTENT WITH EXPECTED VALUES FROM NUMBER OF SAMPLES – GRANULAR PATTERNS

QWG-5, Frascati, May st, 2011 OTT sensitivity Alternative OTT estimation method: stability (2) Impact of atmospheric corrections

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: summary  Adequate data selection techniques + mean angular dependence removal allows to obtain ROBUST OTT estimates WITHOUT introducing systematic errors from imperfect forward model and auxiliary information  Temporal drift effects still need to be accounted for.  Angular dependence of the corrected images is consistent with the original SMOS data  Work in progress:  Use more data  Further analyze latitudinal and temporal variations  New GMF fit using new OTT  Near-future work will compare the goodness of either additive or multiplicative formulations.