De-biased SMOS SSS L3 V7 maps generated by LOCEAN/ACRI-ST Expertise Center
J. Boutin (email@example.com) – J.L. Vergely (Jean-Luc.Vergely@acri-st.fr) – D.Khvorostyanov (firstname.lastname@example.org)
15 February 2022
LOCEAN and ACRI-st work as Ocean Salinity Center of Expertise for CATDS (CATDS CEC-OS) in order to improve methodologies to be implemented in the future in the near real time CATDS processing chain (CATDS-CPDC). They have derived a methodology for correcting systematic SSS biases. Feedbacks from users about the quality of these new products are very welcome, as they are experimental.
This seventh version of Level 3 SMOS SSS covers the period January 2010-November 2021. A correction of SMOS SSS from systematic biases uses an improved ‘de-biasing’ technique: with respect to earlier versions (see a full description of the version 2 method in Boutin et al. RSE 2018), the algorithm for computing the relative biases is unchanged, but the adjustment of the long term mean biases has been slightly updated leading to local improvements in very variable areas as well as in noisy areas (in particular high latitudes and RFI contaminated areas). Validation reports of the product compared to various sources of in situ measurements are available at PIMEP (https://www.salinity-pimep.org). At global scale, without any filtering, r2 between CEC v7 SSS and Argo delayed time SSS are 0.949 (9-day products; 0.940 with CEC V5) and 0.971 (18-day products; it was 0.968 with CEC V5), robust std of the difference is 0.27 (9-day products; 0.29 with CEC V5) and 0.19 (18-day products; it was 0.20 with CEC V5).
The successive evolutions of the corrections are recalled below:
Corresponding CATDS Near Real TIME products
New L1 processing (v7, gibbs 2 algorithm); New L2 processing (v7, New dielectric constant model and specific RFI filtering for deriving L2 SSS), correction for rain instantaneous effect ; debiasing method similar to V5 but with biases estimated over different period/regions
Better stability. Reduced latitudinal seasonal biases and RFI contamination.
L3Q products (RE07 and real time CPDC processings since end May 2021)
Intermediate release, not distributed
=V4 + refined absolute correction
Decrease of biases in very variable and noisy regions (high latitudes, RFI contaminated areas)
=V3 + wind speed limited to 16m/s, Acard filtering, update of SST correction in cold waters, refined absolute correction
Decrease of mean bias over the open ocean, improved ice filtering, improved SSS at high latitudes (especially in the Southern Ocean)
= V2 + SSS natural variability varying seasonally; latitudinal bias correction applied everywhere; SSS correction at low SST; improved absolute correction
=V2 + improved adjustment of land-sea biases close to coast; adjustment of high latitudinal biases
L3Q products (RE06 and real time CPDC processings)
= V1 + SSS natural variability varying spatially; no latitudinal bias correction outside 47S-47N
= V1 + improved land-sea contamination in very dynamic areas
Boutin et al., RSE, 2018
No longer available
= V0 + seasonal latitudinal correction (same SSS natural variability everywhere)
= V0+ Reduced latitudinal biases
Reduced land-sea contamination
Kolodziejczyk et al., 2016
Introduction to the ‘De-biasing’ method:
When considering monthly SSS anomalies, with respect to a SMOS monthly climatology, the precision of SMOS SSS monthly anomalies is on the order of 0.2 pss (Boutin et al. 2016); working in terms of monthly anomalies, removes most of the biases occurring around continents and varying latitudinally. In view of these good results, we have developed a method that corrects SMOS SSS systematic biases by preserving the temporal SMOS SSS dynamic. We recall at the end of this note the principle of the method. In version 7, the algorithm for computing the relative biases is as in version 5, but the adjustment methodology of the latitudinal biases has been slightly updated by enlarging the regions chosen as reference at high latitudes. ISAS20 (Kolodziejczyk et al., 2021) and ISAS delayed time SSS up to 2021 (http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b) have been used for long term bias computation. Moreover, a correction for rain instantaneous effect (estimated in 1mm hr-1 IMERG rain rate classes, see Fig5 of Supply et al., 2020) has been applied on the SSS before the biases correction. Hence, in rainy areas, CEC v7 products are close to a bulk salinity.
Main improvements of CEC V7 products with respect to CEC V5 are a better stability of the SSS and reduced RFI contaminations.
The V7 maps are provided every 4 days from 01/2010 to 11/2021 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 9 days (9 day product) and of 18 days (18 days product). Maps are at a spatial resolution 25x25km2; a mean over neighbor pixels at less than 30km is applied. 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).
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-sea 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 the 2013- 2020 period of SMOS observations. Indeed, it is possible to build salinity time series for each grid point obtained in various observation conditions (depending on the orbit direction and at various distance from the center of the track) 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 further than 800km from the coast (in the Pacific Ocean up to v2, in the Atlantic Ocean in v3,v4 and v5, in the Atlantic + Pacific Ocean in v7) after having empirically corrected a SST-dependent bias related to dielectric constant model issue based on Zhou et al (IEEE TGRS, 2017) in v3 and Dinnat et al. (Remote sensing, 2019) in v4 and v5 and new dielectic constant model (Boutin et al. 2021) in v7. We look for the dwell line (i.e. across track position) the least affected by latitudinal biases (at the center of the swath, ascending orbits) and we adjust all the SSS for a latitude and time varying bias estimated from biases averages with respect to the reference dwell line. 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 within a given range (defined by the SSS natural variability plus the SMOS SSS noise) during a given period, then, the different satellite passes crossing the same pixel during the given period should give consistent salinities. Additionally, 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 satellite passes. In the CATDS CEC LOCEAN debiased products version 0 (delivered in March 2015) only systematic biases near continents were removed. Version 1 (delivered in July 2016), has been updated to remove a latitudinal bias. The main difference between the debias_v1 version and the debias_v2 version (delivered in May 2017), is the SSS natural variability between the various SMOS SSS measured within 18 days at the same latitude, longitude: in debias_v2 version, we take into account an estimate of the natural variability expected from SMOS observed SSS while in debias_v1 version only a geographical constant noise on SMOS SSS was considered. In version 3 to 7, the natural SSS variability varies spatially and seasonally. Hence the v3 to v7 versions better preserves SSS natural variability especially close to river plumes. Note that the across track relative bias estimate does not use any external climatology. It allows minimizing relative biases between SMOS SSS retrieved at various distances across track and on ascending or descending orbits.
These relative salinity variations are then converted, in a last step, to salinities by adding a single constant determined, in each pixel, from SSS statistical distribution over the whole period (SMOS SSS distribution compared to ISAS SSS (see a description of ISAS methodology on http://www.umr-lops.fr/SNO-Argo/Products/ISAS-T-S-fields). This last step only determines the absolute SSS calibration in each grid point; the SMOS SSS temporal variation is independent of this adjustment. Up to version 2, the median of SMOS SSS over the whole study period was adjusted to the median of ISAS SSS. In version 3 and 4, in order to avoid incorrect adjustments in very dynamical river plumes not well captured by Argo floats and hence by ISAS optimal interpolation, the adjustment is made using upper quantiles (80% in version 3, 70 to 90% in version 4, 50% to 80% depending on SSS variability in versions 5 and 7) of ISAS and SMOS SSS distributions over the considered bias calculation period (2011-2017 in v3, 2012-2018 in v4 and v5, 2013-2020 in v7).
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.
Boutin J., Vergely J.L., Marchand S., D'Amico F., Hasson A., Kolodziejczyk Nicolas, Reul Nicolas, Reverdin G., Vialard J. (2018). New SMOS Sea Surface Salinity with reduced systematic errors and improved variability. Remote Sensing of Environment, 214, 115-134. Publisher's official version : http://doi.org/10.1016/j.rse.2018.05.022 , Open Access version : http://archimer.ifremer.fr/doc/00441/55254/
Boutin Jacqueline, Vergely Jean-Luc, Dinnat Emmanuel P., Waldteufel Philippe, D'Amico Francesco, Reul Nicolas, Supply Alexandre, Thouvenin-Masson Clovis (2021) : Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE Transactions on Geoscience and Remote Sensing, Volume 59, Issue 9, Pages 7256-7269. Publisher's official version: https://doi.org/10.1109/TGRS.2020.3030488, Open Access version : https://archimer.ifremer.fr/doc/00657/76943/
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.
Kolodziejczyk Nicolas, Prigent-Mazella Annaig, Gaillard Fabienne (2021). ISAS temperature and salinity gridded fields. SEANOE. https://doi.org/10.17882/52367
Supply, A., J. Boutin, G. Reverdin, J.-L. Vergely, and H. Bellenger, 2020: Variability of satellite sea surface salinity under rainfall. In: Satellite Precipitation Measurement, V. Levizzani, C. Kidd., D. B. Kirschbaum, C. D. Kummerow, K. Nakamura, F. J. Turk, Eds., Springer Nature, Cham, Advances in Global Change Research, 69, 1155-1176, https://doi.org/10.1007/978-3-030-35798-6_34.
Boutin J., Vergely J.-L., Khvorostyanov D. (2022). SMOS SSS L3 maps generated by CATDS CEC LOCEAN. debias V7.0. SEANOE. https://doi.org/10.17882/52804#91742.
The CATDS data are freely distributed. However, when using these data in a publication, please use the following reference and acknowledgement :
Boutin J., Vergely J.-L., Khvorostyanov D. (2022). SMOS SSS L3 maps generated by CATDS CEC LOCEAN. debias V7.0. SEANOE. https://doi.org/10.17882/52804#91742
"The L3_DEBIAS_LOCEAN_v7 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 Traitement 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)."
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password : catds2010
chdir : Ocean_products/L3_DEBIAS_LOCEAN
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