Debiased SMOS SSS L3 v1 maps generated by LOCEAN/IPSL/ACRI-ST
J. Boutin (email@example.com)– JL. Vergely (Jean-Luc.Vergely@acri-st.fr) – S. Marchand (firstname.lastname@example.org)
5 July 2016
In order to propose improved methodologies to be implemented in future CATDS-CPDC versions, LOCEAN/IPSL (UMR CNRS/UPMC/IRD/MNHN) and ACRI-st have recently derived a methodology for correcting systematic SSS biases. We welcome feedbacks from users about the quality of these new products, as they are experimental.
We have shown that, when considering monthly SSS anomalies, with respect to a SMOS monthly climatology (built from the SMOS_LOCEAN_v2013 products available at CATDS CEC LOCEAN), the precision of SMOS SSS monthly anomalies is on the order of 0.2 (see Boutin et al. 2016); in particular, working in terms of anomalies, removes most of the biases occuring around continents. In view of these good results, we have developped a first attempt to correct SMOS SSS systematic biases by preserving the temporal SMOS SSS dynamic (Kolodziejczyk et al., 2016). This method was the basis for the debiased products v0 delivered at CATDS CEC LOCEAN in March 2015. It has now been updated to be applied on CATDS RE04 reprocessed products (similar to ESA L2OS version 6 products) and it now includes a latitudinal bias removal correction. We recall at the end of this note the principle of the method. It has been validated using ship measurements from French Sea Surface Salinity Observation Service (http://www.legos.obs-mip.fr/observations/sss). An example of the comparisons is shown below:
Figure 1 : top)mean difference of SSSsmos minus SSSship as a function to the distance to coast for various SSS products; bottom) standard devation of the difference. Statistics of SMOS v1 debiased products are reported in purple. With respect to the non debiased SMOS products, mean difference as well as standard deviation of the difference are reduced whatever the distance from the coast and they become close to the one of the Argo interpolated product (ISAS).
These maps are provided every 4 days from 07/2010 to 03/2016 and are derived from a combination of ascending and descending orbits. Debiased SSS are temporally averaged using a slipping Gaussian kernel with a full width at half maximum of 18 days. They are spatially averaged over a radius of 50km, and oversampled over a 0.25x0.25° grid. They also contain an estimation of the mean error of the salinities (field eSSS) obtained from the spatial standard deviation of the SSS in the 50km radius around each grid node. This error estimate also contains spatial natural variability and should only be considered as a qualitative indicator (e.g. larger error expected in areas contaminated by RFI). Because SSS biases are not stable at high latitude, and because an ice mask has not been implemented yet, a mask has been applied at latitudes north of 65°N and south of 55°S. Users must be very cautious in regions with large eSSS as, for instance, RFIs pollutions are not always completely removed.
Summary of the methodology:
The SMOS sea surface salinities (SSS) are affected by biases coming from various unphysical contaminations such as the so-called land contamination and latitudinal biases likely due to the thermal drift of the instrument. These biases are relatively weak and have almost no impact on soil moisture retrieval. On the contrary, for salinity estimation, the impact is non negligible and can reach more than 1 salinity unit in some regions close to the coasts.
These biases are not easy to characterize because they exhibit very strong spatial gradients and they depend on the coast orientation in the Field Of View (FOV). Moreover, these biases are dependent on the position on the swath.
The zero order bias is the so-called Ocean Target Transformation (OTT) which is a correction applied at brightness temperature level. Here, we consider remaining biases on the SSS retrieved from brightness temperatures corrected with an OTT. SSS maps are obtained from a correction applied at salinity level. This correction is determined using simultaneously the July 2010-March 2016 period of SMOS observations. Indeed, it is possible to build salinity time series for each grid point depending on the observation conditions (for instance depending on the orbit direction) and check, from a statistical point of view, the consistency of the salinities.
The first step of this empirical approach is to characterize as accurately as possible these biases as a function of the dwell line position. We first characterize the seasonal variation of the latitudinal biases using SSS in the Pacific Ocean further than 800km from the coast. We look for the dwell line (i.e. across track position) the least affected by latitudinal biases (at XXkm for the center of the swath) and we adjust all the SSS for a latitude and time varying bias estimated from biases averages with respect to the reference dwell line in the Pacific Ocean. The second step is to correct for biases in the vicinity of land. We have found that these biases vary little in time, and can be characterized according to the grid point geographical location (latitude, longitude) and to its location across track. If we assume that the salinity at a given grid point varies very slowly during a given period, then, the different satellite passes crossing the same pixel during the given period should give consistent salinities. Additionnally, assuming that the bias does not vary temporally for a given grid point implies that the relative salinity variation over the whole period should be the same whatever the distance to the center of the track. It is then possible to estimate the relative biases between the various distances across track and to obtain, with a least squares approach, a time series of relative salinity variations obtained from all the passes. Note that these steps estimation do not use any external climatology. It allows checking that all the dwell-lines and orbit types (ascending or descending) give consistent results.
These relative salinity variations are then converted, in a last step, to salinities by adding a single constant determined, in each pixel, using an average SSS climatology over the whole period (ISAS v6.2 data). This last step, because it uses only one SSS climatology per grid point as reference totally preserves the SMOS temporal dynamic.
More information on ESA OS-L2 products can be found on http://www.argans.co.uk/smos/.
Alory G., T. Delcroix, P. Téchiné, D. Diverrès, D. Varillon, S. Cravatte, Y. Gouriou, J. Grelet, S. Jacquin, E. Kestenare, C. Maes, R. Morrow, J. Perrier, G. Reverdin and F. Roubaud, 2015. The French contribution to the Voluntary Observing Ships network of Sea Surface Salinity. Deep Sea Res., 105, 1-18, doi:10.1016/j.DSR.2015.08.005.
Boutin, J., N. Martin, N. Kolodziejczyk, and G. Reverdin, 2016, Interannual anomalies of SMOS sea surface salinity, Remote Sensing of Environment, doi:http://dx.doi.org/10.1016/j.rse.2016.02.053.
Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin Mitigation of systematic errors in SMOS sea surface salinity, 2016, Remote Sensing of Environment, doi:http://dx.doi.org/10.1016/j.rse.2016.02.061.
The CATDS data are freely distributed. However, when using these data in a publication, we request that the following acknowledgement and a reference the above publications be given:
"The L3_DEBIAS_LOCEAN_v1 Sea Surface Salinity maps have been produced by LOCEAN/IPSL (UMR CNRS/UPMC/IRD/MNHN) laboratory and ACRI-st company that participate to the Ocean Salinity Expertise Center (CECOS) of Centre Aval de Traitemenent des Donnees SMOS (CATDS). This product is distributed by the Ocean Salinity Expertise Center (CECOS) of the CNES-IFREMER Centre Aval de Traitement des Donnees SMOS (CATDS), at IFREMER, Plouzane (France)."
The CATDS-CEC Locean research products are freely available on FTP :
user : ext-catds-cecos-locean
password : catds2010
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