ocean salinity

SMOS QWG-5, 30 May- 1 June 2011, ESRIN

Ocean Salinity

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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–TEAMSMOS Barcelona Expert CentrePg. Marítim de la Barceloneta 37-49, Barcelona SPAINE-mail: [email protected]: www.smos-bec.icm.csic.es

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 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)


Tb ascending maps


Tb descending maps


Impact on SSS

SSS 3 semi-orbits

Run with patched L1PP and L2OS 3.17

Specific OTT computed from uncorrected and corrected L1


Uncorrected


Corrected


Uncorrected


Corrected


Conclusion

The correction has removed the first order problem (strongest signal)

Back to the original scene dependant bias issue (A. Camps 2005)?

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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

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New semi-empirical roughness model


New semi-empirical roughness model

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QWG-5, Frascati, May 30-31st, 2011

OTT sensitivity study

J. Gourrion, M. Portabella, R. Sabia, S.Guimbard

SMOS-BEC, ICM/CSIC


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


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

Impact of number of snapshots


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.


Ocean/ice transition

Salinity ? Rain ?

Roughness residuals ?New model 3SSA/SPM model

OTT sensitivity

Latitudinal variability

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.


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)


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

BUTselect 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)


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.

ocean salinity

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