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Tropical Atlantic SSS- 2010 seasonal cycle as seen from SMOS using L1 data from the first reprocessing

To assess the quality of the first version of the ESA operational Level 2 SSS swath products, satellite/in situ SSS data match-ups were collected over the second half of 2010. This database reveals that 95% of the SMOS Level 2 products show a global error standard deviation on the order of ~1.3 practical salinity scale (pss). Simple spatio-temporal aggregation of the Level 2 products to generate monthly SSS maps at 1°x1° spatial resolution reduces the error down to about 0.6 pss globally and 0.4 pss in the tropics for 90% of the data. Several major local problems were however detected in the products. Systematically, SMOS SSS data are too salty within a ~1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. This spurious signal is present when brighter land or ice masses are located in the extended field of view domain of the antenna. Numerous world ocean area are permanently or intermittently contaminated by radio-frequency interferences (rfi), particularly in the northern high latitudes and following Asia coastlines. Moreover, critical spatio-temporal drifts in the retrieved SSS fields were found with varying signatures in ascending and descending passes. The detailed quality assessment of these SMOS first operational SSS products for the second half of 2010 can be found in Reul et al, 2011.

Recently, the complete year 2010 of SMOS L1A (calibrated visibilities) and L1B (brightness temperatures) data have been reprocessed by ESA with fixed calibration parameters to tentatively better understand the sources for the several problems encountered in SMOS Level 2 products, in particular over the oceans.  During the analysis of this dataset, it was found that the major sources of errors in SMOS Level 2 products :i.e.: spatio-temporal drifts, biases between ascending and descending passes and land-contamination were mostly induced by several bugs in the L1A to L1B processors. While some residual differences between ascending and descending passes may be attributed to badly modeled geophysical contributions (such as galactic signal reflections), most of the spatio-temporal drifts observed in the SSS at Level 2 can be attributed to an erroneous correction of the direct sun impact at L1 and an unaccurate thermal model for the instrument. In addition, a bug in the weight of the visibility at zero baseline (V(0,0)) was also found to be responsible for the very significant land-contamination observed in the operationnal L2 products. New SSS products were therefore derived at CATDS/C-EC based on the reprocessed L1A data but using a simplified L1A to L1B processor in which the direct sun correction was omitted and the V(0,0) bug corrected.
Because the direct sun aliases contamines the Extended Field of View of the antenna, the SSS retrievals were limited to the Alias-free Field of view.
Level 3 daily and monthly SSS fields were produced from this new L1B data set at global scale with a spatial resolution of 25 km. A 10-days and 50 km moving-window average was further applied to the daily data.

click on the image above to see the gif animated

In the animation above, we show the new SSS products evolution over the Tropical Atlantic region combining both asc and desc passes from January to December 2010. Note that only L1 data acquired in Full-polarisation were used to generate these new Level 3 SSS. This explains the numerous gaps in the SSS data during the commissionning phase from January to end of May. A simple and preliminary Radio Frequency Interference filter was as well applied to the data, so that RFI contaminated data may still be present, particularly along the west coasts of Africa. In addition, the direct sun aliases are sometimes located at the border of the Alias Free domain, particulary at the end of the year (November to December) in descending passes. Note that these solar contributions that were not corrected for in our processing  produce some spurious fresh stripes in the SSS data at the end of the year.

Preliminary comparison of the new monthly-averaged SMOS SSS with Thermosalinograph data acquired from Research Vessels and ships of opportunity that crused in the Tropical Atlantic in 2010 are provided for each month in the figures below:


MARCH 2010
APRIL 2010

MAY 2010
JUNE 2010

JULY 2010



Further discussions, results and additional comparison of these new SMOS SSS products with in situ data will be provided soon on this web site.