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## Ocean Salinity

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### Ocean Salinity

### Land sea contamination correction

Commissioningreprocessinganalysis

New processorversion: improvements and problemsdetected/solved

Present performance

Futureevolution: ongoingstudies

J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM

SMOS Barcelona Expert Centre

Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN

E-mail: jfont@icm.csic.es

URL: www.smos-bec.icm.csic.es

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 17-21 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)

Impact on SSS

- SSS 3 semi-orbits
- Run with patched L1PP and L2OS 3.17
- Specific OTT computed from uncorrected and corrected L1

Conclusion

- The correction has removed the first order problem (strongest signal)
- Back to the original scene dependant bias issue (A. Camps 2005)?

Pre-launch semi-empirical roughness model (SSS3) was derived from data obtained during the WISE experiments (2000-2001) 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

New semi-empirical roughness model

OTT sensitivity

- DPGS OTT
- Impacton OTT quality of differentfactors:
- Number of snapshotsused
- Temporal variability and apparentdrift
- Latitudinal variability
- Alternative OTT estimationstrategy
- Methodand preliminaryresults

OTT sensitivity

Impact of number of snapshots

- For a 16-days period dataset (Aug. 3rd – Aug 18th), about 12000 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. 19th – Sep 3rd, Sep. 4th – Sep 19th). 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

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 = 6250.
- The OTT rms increase (relative to reference) indicates an increasing data inconsistency with time, i.e., apparent drift.

OTT sensitivity

Latitudinal variability

Salinity ?

Rain ?

Roughness residuals ?

New model 3

SSA/SPM model

- A 16-day period dataset (Aug. 3rd – Aug 18th) 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.

Ocean/ice

transition

OTT sensitivity

OTT as mean departure from full forward model: summary

- OTT robustnesssignificantlydependsonsampling. Current OTT computationshould use a largernumber of snapshots.
- Temporal inconsistenciesdueto non-modelled instrumental/reconstructioninstability and imperfectForeignSourcesmodelling
- Latitudinal inconsistenciesduetoimperfectmodellingorauxiliaryparameters
- OTTsestimatedfromdifferentdatasetswillvarydependingonthedistribution of sampledgeophysicalconditions
- Withcurrent OTT methodology, the data are adjustedto reproduce the mean forward modelbehaviour (e.g., angular dependency): updated forward models are NOT independentfrom pre-launchversions (usedto compute theOTT)

OTT sensitivity

New OTT estimation method: basics (1)

- 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.

OTT sensitivity

New OTT estimation method: comparison

INCONSISTENT ANGULAR DEPENDENCE

BETWEEN SMOS DATA AND

PRE-LAUNCH FORWARD MODELS

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

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.

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