Consistency in the salinity retrievals from Aquarius and SMAP is crucial to achieve continuity between the two sensors, which is necessary for scientific studies. Our presentation gives an account of the RSS/NASA Version 3 SMAP SSS release and examines its consistency with the Aquarius V5 end-of mission salinity product.

The major feature in the SMAP Version 3 release is the use of the Aquarius Version 5 geophysical model function, which has been adapted to SMAP. In particular, Aquarius Version 5 and SMAP Version 3 use the same models for the dielectric constant, atmospheric oxygen absorption, surface roughness correction, Faraday rotation and reflected galactic radiation. This results in consistent geophysical salinity retrieval algorithms for the two sensors.

A correction for intrusion of land surface emission into the antenna sidelobes has been developed for SMAP V3, which follows the same procedure as the sidelobe correction in Aquarius V5. It allows SMAP salinity retrievals up to 40 km from the coast.

Several important differences between the two sensors need to be taken into account when making comparisons between the Aquarius and SMAP salinity retrievals.

The SMAP antenna is 1% emissive, whereas the Aquarius antenna was not emissive. This causes significant spurious biases in the SMAP salinity data that correlate with the physical temperature of the antenna and thus depend on solar heating. The thermal model for the temperature of the reflector is inaccurate during eclipse when rapid heating and cooling of the dish occurs. It is necessary to develop an empirical correction for this spurious emissivity signal.

Due to the demise of the SMAP radar, SMAP does not provide valuable L-band scatterometer wind speeds at the same location and time as the radiometer observation as Aquarius did. Therefore, the SMAP salinity retrieval algorithm needs to use an external ancillary wind speed for correcting the surface roughness effect. SMAP V3 uses wind speeds and direction from the near-real time Cross Calibrated Multi-Platform (CCMP) Level 4 product. The CCMP is based on a variational assimilation of multiple Level satellite winds form the RSS Version 7 Ocean Suite (WindSat, ASMR2, SSMIS, GMI) together with a background field from 0.25° NCEP GDAS.

SMAP performs a full 360° scan, and thus directional biases average out. It is important to stratify the satellite observations by look direction when comparing SMAP with Aquarius.

We will provide a comparison between the RSS V3 SMAP and the Aquarius V5 salinities and will assess how well they correspond to ground truth measurements from ARGO drifters and moored buoys. Both products are largely free of seasonal and zonal biases, but small differences in certain regions remain. This comparison will allow us to examine the consistency between the two sensors and point to necessary improvements in the future.

The Baltic+ Salinity Dynamics aims at advancing research and consolidating existing developments towards the generation of a dedicated Sea Surface Salinity (SSS) product suited for Baltic Earth research (https://www.baltic-earth.eu/organisation/bewg_salinity/index.html). The Baltic Sea is one of the most challenging regions for the satellite SSS retrieval. Nowadays, the availability of SMOS SSS products is quite limited over this region (temporal and spatially) due to several technical limitations related to the low sensitivity of L-band TB at SSS changes in cold waters, land-sea and ice-sea contamination, and high contamination by Radio-Frequency Interferences (RFI) sources. The assessment of the quality of the SMOS SSS products in the Baltic Sea is also an issue. On one hand, the basin is strongly stratified and therefore, differences between the first centimetres (i.e. SMOS measurements) and the few meters (i.e. measurements provided by in situ) can be noticeable. On the other hand, the basin is very shallow and then, the near surface vertical salinity gradient must be estimated from different sources. There are also seasonal salinity variations in the surface, which are caused by melting of ice and spring floods from rivers. Thus, the representativeness of satellite salinity must be assessed. Besides, some parts of the Baltic Sea such as the Gulf of Finland have estuarine regimes and therefore exhibit relatively strong horizontal salinity gradients.
In the recent years, the BEC team has developed innovative algorithms for improving the quality of brightness temperatures and sea surface salinities dealing with the main processing issues: (i) Nodal Sampling [1] to mitigate the RFI contamination, (ii) Debiased non-Bayesian SSS retrieval [2] to empirically mitigate systematic biases and improve the spatial coverage of SSS maps, and (iii) Multifractal Fusion technique [3] to improve the temporal and spatial resolution to better describe the salinity dynamics. The application of these techniques has been proved to enhance SMOS SSS products over other critical regions such as the Mediterranean Sea (http://bec.icm.csic.es/ocean-experimental-dataset-mediterranean/).
The work proposed in Baltic+Salinity is aimed at extending the above-mentioned methodologies for developing a dedicated SMOS SSS product, suitable for the needs of the Baltic research community. In a first stage of this project, we will develop a novel prototype SSS product (a series of 3 years of data) and we will analyze the limits and benefits of the newly generated product. We will use different subsets of in situ data for the algorithm calibration (i.e. for fixing different implementation choices and trade-offs) and for the validation of the resulting SSS products. For the algorithm calibration, we will use data from regular monitoring stations round the Baltic Sea (HELCOM dataset). These observation stations have long time series of data, which give perspectives for the temporal and spatial variabilities of the surface salinity field. Data from several Finnish coastal fixed oceanographic stations will be included to describe the coast-open sea gradients. The resulting SMOS SSS products will be validated against in situ data using the open data from ICES, which acts as data centre for HELCOM, SeaDataNet and EMODnet.
We will present at the conference the up-to-date Baltic+ SSS products generated in this project. We will also analyze the impact and added-value of these products with respect to other existing EO-based datasets. The knowledge gained during the Baltic+ Salinity project will be an important added value to the on-going ESA Climate Change Initiative SSS, which aim to create the longest up to date SSS global time series.

[1] V. González-Gambau, E. Olmedo, A. Turiel, J. Martínez, J. Ballabrera-Poy, M. Portabella, and M. Piles, “Enhancing SMOS brightness temperatures over the ocean using the nodal sampling image reconstruction technique,” Remote Sensing of Environment, vol. 180, pp. 205 – 220, 2016, special Issue: ESA’s Soil Moisture and Ocean Salinity Mission - Achievements and Applications.
[2] E. Olmedo, J. Martínez, A. Turiel, J. Ballabrera-Poy, M. Portabella, “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity”, Remote Sensing of Environment, vol. 193, no. Supplement C, Pages 103-126, 2017.
[3] Olmedo, E., Martínez, J., Umbert, M., Hoareau, N., Portabella, M., Ballabrera-Poy, J., Turiel, A., 2016. "Improving time and space resolution of SMOS salinity maps using multifractal fusion". Remote Sens. Environ. 180, 246–263.

This presentation aims at inventorying and quantifying the sources of differences between satellite Sea Surface Salinity (SSS) and in situ near surface salinity (Near_SS), in order to estimate an error budget for the Soil Moisture Ocean Salinity (SMOS) SSS.
A common way used to estimate the uncertainty on SMOS SSS is to compare level 3 SMOS SSS averaged over typically one month and 50-100km with Argo derived Near_SS such as ISAS (Gaillard et al. 2016) and ship Near_SS (Alory et al. 2015). We study to which extent the observed root mean square differences between SMOS SSS and in situ N_SS can be explained by the various components of the error sources:
- The undersampling of the in situ measurements within a satellite pixel and the difference between SSS and Near_SS due to the vertical stratification of the salinity between typically 5m and 1cm depth are significant sources of difference that may reach several tenths of pss; this error will be roughly estimated in regions well sampled by in situ N_SS and using model simulations published in the open literature as reviewed in (Boutin et al. 2016),
- Radiometric noise and errors of the auxiliary parameters involved in the retrieval of the SMOS SSS are the source for a ‘theoretical’ error (Etheo) on SMOS SSS that is inferred from the sensitivity of the L-band brightness temperatures (Tb) to SSS, Sea Surface Temperature (SST) and wind speed (WS). Etheo are provided in SMOS level 2 SSS product.
- In addition to Etheo, the normalized Chi2 (NChi2) (the quadratic difference between measured Tb and Tb simulated with the L-band radiative transfer model and the retrieved parameters, normalized by the number of independent measurements) informs on the quality of the fit, i.e. on the realism of the radiative transfer model relative to the radiometric measurements which can be affected by extra noise coming for instance from radio frequency interferences. The combination of Etheo and NChi2 provides a more realistic uncertainty estimate on SMOS SSS than Etheo.
- Systematic errors related to effects such as land-sea contamination, sun glint, or imperfect galactic noise corrections limit the accuracy of SMOS SSS and can be mitigated using self-consistency criteria on SMOS SSS measured at the same location within a close temporal interval (Boutin et al. 2018). In this presentation we will consider the latest Level-3 Centre Aval de Traitement des Données SMOS (CATDS) products, before and after removal of the systematic biases.

This error budget analysis is an important step towards the characterization of the uncertainties of the satellite SSS. It will be further used to allow to select the best models and ancillary datasets between the available L-band missions (CCI+SSS project).

This work participates to the Satellite & In Situ Salinity (SISS) Working Group: Understanding Stratification and Sub-Footprint Processes.

References

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., Vergely J. L., Marchand S., D'Amico F, Hasson A., Kolodziejczyk N., Reul N., 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 : https://doi.org/10.1016/j.rse.2018.05.022

Gaillard, F., T. Reynaud, V. Thierry, N. Kolodziejczyk and K. von Schuckmann (2016), In situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height, J. Clim., 29, 1305-1323, doi:10.1175/JCLI-D-15-0028.1.

Abstract: The recent emergence of satellite-based sea surface salinity (SSS) measurements provides new opportunities for oceanographic research on freshwater influence in coastal environments. Several products currently exist from multiple observing platforms and processing centers, making product selection for different use cases challenging. Here we evaluate four popular SSS datasets in the Gulf of Mexico (GoM) to characterize the error in each product versus in-situ observations: two products from NASA’s Soil Moisture Active Passive (SMAP) mission processed by Remote Sensing Systems (REMSS) (40 km and 70 km); one SMAP 60 km product from the Jet Propulsion Laboratory (JPL); and one 60 km product from ESA’s Soil Moisture Ocean Salinity (SMOS) mission. Overall, the four products are remarkably consistent on seasonal time scales, reproducing dominant salinity features. Towards the coast, 3 of the 4 products (JPL SMAP, REMSS 40 km SMAP and SMOS) show increasing salty biases (reaching 0.7-1 pss) and RMSD (reaching 1.5-2.5 pss) and a decreasing signal to noise ratio from 3 to 1. REMSS 40 km generally shows a lower RMSD than other products (~ 0.5 vs. ~1.1 pss) in the nearshore region. However, at some buoy locations, SMOS shows the lowest RMSD values but has a higher bias overall (>0.2 vs. <0.1 pss). The REMSS product is not consistent in terms of data availability in the nearshore region and performs poorly within 100 km of the coast, relative to other products. Additional analysis of the temporal structure of the errors over a range of scales (8/9-day to seasonal) shows significantly decreasing RMSD with increasing timescales across products.

The SMOS Pilot Mission Exploitation Platform (Pi-MEP) for Salinity is an ESA initiative to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over ocean. The Pi-MEP for Salinity motivation is twofold:

i) To serve as an enhanced validation platform, complementing and expanding the efforts of the SMOS Expert Support Laboratories (ESLs);
ii) To offer a testbed to enable oceanographic process studies in different domains, capitalizing on SMOS salinity data in synergy with additional satellite estimates (e.g., SST, WS, SSH, currents, rain estimates).

The Pi-MEP is becoming a reference hub for the SMOS SSS mission providing access altogether to other satellite (Aquarius, SMAP) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, SST).
Match-up databases of SMOS/in situ (ARGO, TSG, moorings, drifters) data and inter-comparison reports at different spatiotemporal scales are systematically generated, and recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.
The Platform is also meant to monitor SSS in selected oceanographic “case studies”, ranging from tropical river plumes monitoring to SSS characterization at high latitudes or in semi-enclosed basins.

Instrumental to the activities of the SMOS Pi-MEP Salinity is a dedicated Scientific Advisory Group (SAG), which has provided valuable inputs to set up the Platform by helping defining user requirements and by providing scientific advice in two consultation meetings.
SAG members are currently serving as super-users of the Platform functionalities in the recently launched pre-operational phase (in concomitance to the Ocean Salinity Science 2018 Conference). Once fully consolidated, and profiting from the continuous feedback gathered upon the beta-version of the tools, in May 2019 during the ESA Living Planet Symposium 2019 the Platform will be released in its operational fashion and therefore fully accessible to the wider oceanographic community for their validation/exploitation studies.

Concomitant efforts in a potential partnership with NASA in this context, and a joint venture with the SISS Working Group in regard to the updated estimation of the SMOS RMSE will also be described.

Sea Surface Salinity (SSS) has a strong impact in many climate-related processes. For instance, SSS reflects the balance of evaporation and precipitation at ocean surface, and provides valuable information about water vapor and energy fluxes between the ocean and the atmosphere that drive the long-term evolution of climate. SSS samplings by in situ devices are quite scattered and can only resolve processes well above the oceanic mesoscale, which is insufficient in particular for the characterization of horizontal and vertical fluxes. On the other hand, SSS derived by remote sensing means provides adequate space and time sampling, but the time series are relatively short for some climate applications, and in particular to detect and characterize climate trends.

It is possible to use the existing series of remote sensing SSS observations to reconstruct SSS maps at times prior to the advent of satellite SSS observations. One option is to assimilate existing SSS maps in a numerical model of the ocean and to integrate backwards in time. This approach does not benefit from all the information of SSS series but just of the relatively short period that the data assimilation scheme can influence numerical integration – of the order of weeks, at most. For the same reason, and due to the approximative nature of parametrizations, the influence of data assimilation rapidly fades away, and therefore, the quality of the reconstructed SSS data relies on the quality of the physics implemented in the numerical model.

A completely different approach was introduced in Droghei et al., Front. Mar. Sci. (2018). In that reference, the authors build a multivariate optimal interpolation (OI) combining Sea Surface Temperature (SST) and SSS, then they exploit the connection between both variables to reconstruct past SSS using existing SST maps and the known OI matrices. The limitations of such approach are that the statistical relations between SSS and SST are assumed to be stationary, the linearization of the relations between SSS and SST, and that the requirements for constructing statistically significant OI matrices degrade the effective resolution of the time-extrapolated SSS maps. Nevertheless, the quality of the reconstructed SSS is very reasonable when validated with independent in situ data.

In this work, we propose to go a step forward. We use the multifractal fusion approach first proposed by Umbert et al., Rem. Sen. Env. (2014), to derive the maps of scalar fusion parameters that relate SSS and SST over a given overlap period, with the goal of using those fusion parameters to reconstruct SSS back on time. Multifractal fusion parameters generalize the concept of T-S curves characteristic to water masses, providing a more accurate, geographically localized description of the link SST-SSS: those maps of multifractal fusion parameters have the same resolution as SST maps, thus providing a very detailed geographical description of the dynamic links between SST and SSS and also an improved resolution of the reconstructed maps. Besides, due to the special characteristics of the multifractal fusion parameters (their spatial regularity is granted by construction), they evolve very slowly in time, providing a very stable scheme for reconstructing the sea state of the past.

With the multifractal fusion parameters derived from the period of SST-SSS period, we used fixed versions of those parameters applied to long series of SST maps to construct semi-climatologies of SSS. We denote this reconstruction of past states of SSS by “semi-climatologies” because the dynamic compensation between SSS and SST, which is contained in the map of multifractal fusion parameters, is assumed fixed, but the map of SST used to reconstruct SSS corresponds to the specific date at which we want to reconstruct SSS. The SSS maps generated that way contain all the dynamic of SSS than can be explained by its typical dynamic adjustment with SST, but not any transient event which departs significantly from those “standard conditions”. In summary, those maps have variable conditions introduced by SST, and are thus dynamic, but assuming climatologic adjustment: thus the name “semi-climatological”.

We have derived SSS semi-climatologies, using SST from ESA CCI+ SST project to construct a decades-long series of SSS maps. We will show how those semi-climatological maps compare to standard SSS maps, analyzing and discussing the differences from a climate perspective. We finally discuss the skill of semi-climatological SSS to describe climate-relevant events such as ENSO or IOD.