Seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans (50∘ N–90∘ N) are now available in our website.
Discharge of Mackenzie river captured by BEC Arctic SSS product
The new SMOS SSS maps are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.
A complete description of the methodology used in the generation of this product and a quality assessment can be found in Olmedo et al, 2018, RS (available in https://www.mdpi.com/2072-4292/10/11/1772 ).
Three years ago, the BEC team introduced a new method for the retrieval of SSS from SMOS data: the debiased non-Bayesian approach. The method was first used to derive the first maps of SMOS SSS in the Mediterranean.
After three years, we have extensively validated the method, first in the Mediterranean, then in the Arctic and finally globally.The debiased non-Bayesian approach is now a consolidated technique, and reportedly the one providing the best estimates of SSS using SMOS data in any area of the global ocean.
In the next months, we plan to introduce new improvements. Very soon, we are going to serve an extended series of improved Arctic SSS maps. And in some months from now, we will deliver the first SSS maps ever in a very challenging area: the Baltic Sea. And this is only the beginning: we plan to ensure an almost operational generation of all debiased non-Bayesian SSS maps (that is, the maps will be generated with a short delay, a few weeks at most).
We are pleased to announce the new near real-time global SMOS L3 soil moisture products v003. These products have different averaging periods (and frequency rates): 1-day (daily) maps in both ISEA 4H9 and EASE-2 25 km, and 3-day (daily), 9-day (every 3 days), 1-month (monthly) and 1-year (yearly) maps in EASE-2 25 km. All of them have been generated using the latest version of SMOS L2 soil moisture processor (v650, which supersedes the previous L2 v620). The main improvements of v650 are related to algorithm updates, parameters configuration and auxiliary files changes.
In a continuous effort to improve the quality of our data and provide a better service to our users, we present the new SMOS Sea Ice Concentration (SIC) product for the Arctic Ocean .
The new product is based on the algorithm presented in the paper Gabarro et al., 2017 . The algorithm uses the differences between vertically-polarized brightness temperature (TB) measurements of two different incidence angles (i.e., angular differences or AD) and a Maximum-likelihood estimation to retrieve SIC. This AD index has lower sensitivity to cganfes in ice temperature, ice salinity and thin ice thickess (see  for more details) than the TB measurements, and is therefore more suitable for SIC retrievals.
The daily Arctic Sea Ice Concentration (SIC) product is provided in the NL EASE grid (25km x 25km) and consists of a 3-day averaging of the ascending and descending SMOS Level 1B data provided by ESA (v6.20).
Due to the higher penetration of the L-band signal on the sea ice, SMOS underestimates SIC in the presence of thin ice (less than approx. 70 cm), which usually happens over marginal ice zones and freeze-up periods (October-March). Therefore, the SMOS data should be used taking it into account. The SMOS-derived SIC estimations can complement those from higher-frequency radiometers, yielding to enhanced SIC products.
A more detailed description of the methodology and the product can be found in the Product Description document available from the BEC webpage.
Please, do not hesitate to contact us in case you have any question or comment at email@example.com. Your feedback is most welcome!
Enjoy the products!
 New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator,C. Gabarro, , A. Turiel, P. Elosegui, J.A. Pla-Resina, M. Portabella. The Cryosphere,11:4,1987–2002,2017. DOI: 10.5194/tc-11-1987-2017- https://www.the-cryosphere.net/11/1987/2017/
A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain 6 years of SMOS Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. This product has been developed by the Barcelona Expert Center and the GHER group at University of Liège (Belgium), under the ESA STSE project “SMOS sea surface salinity data in the Mediterranean Sea (SMOS+ Med)”. SMOS+ Med was leaded by Dr. Aida Alvera-Azcarate, from GHER.
The complete description of the methodology as well as the analysis of the quality assessment of the product can be found in Olmedo, E. et al., Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis, Remote Sensing, 2018, 10(3).
Ocean currents play a key role in Earth’s climate – they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean ve- locities in the upper ocean and the available approaches to assimilate this information into ocean models.
To download the published paper click here.
Sea surface temperature from AVHRR. Upper left: absolute dynamic topography from AVISO (black lines) and the associated geostrophic velocities (arrows). Top right: velocities derived from a sequence of thermal images using the MCC method (arrows). Bottom: velocities derived from the thermal image using a Butterworth filter (arrows)
Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored.
In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate.
After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness ≲ 0.6 m), the method underestimates the actual sea ice concentration.
Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor pan-Arctic sea ice conditions.
Demonstrations against the violence all over Catalonia, October 3rd, 2017
On October 1st, 2017, many Catalans waited in front of the voting stations to participate in a referendum to decide the future of Catalonia. The Spanish Constitutional Court had suspended the referendum, but nevertheless the regional government decided to go ahead with the poll. The response by the Spanish Government was to concentrate in Catalonia a massive amount of anti-riot police squads during the previous days, with the order of prevent the voting to take place. Many were convinced that they would never dare to attack the peaceful hundreds of thousands of citizens, that they will just take the ballots and ballot boxes away, and that the voting day will be just a political demonstration, a tour de force between Catalan independentists and the Spanish Government. They were deadly wrong.
The extreme use of the force by the Spanish policemen terrified the people that was just standing up in front of them, raised arms and singing. The media have reproduced horrifying witnesses of the brutal, unjustified and disproportionate use of the strength against the population that just wanted to express a political opinion. Many of us at BEC know well what happened, as we were at the poll stations and saw the indiscriminate use of violence or waited in the lines in the anguish of knowing that they could appear at any time and attack us in sight with no reason.
BEC does not endorse any political position, as in our team all the opinions can be found; but this disparity of opinions does not prevent a friendly respect of each other, as it happens in mature democratic societies. This has nothing to do with what we saw past Sunday.
The BEC team
Past June 19th 2017 we celebrated the 10th anniversary of the foundation of the Barcelona Expert Center.
We were honored of counting with the presence of the Minister of Agriculture, Livestock, Fishing and Food of Generalitat de Catalunya, Ms. Meritxell Serret, and of the deputy Vicepresident for Scientific-Technical Areas of CSIC, Dr. Victoria Moreno, who highlighted the institutional importance of BEC for CSIC and for Catalonia.
Objectively Analysed SSS for the period May 27th to June 4th, 2014
In a continuous effort to improve the quality of our data and provide a better service to our users, we have made a new brand of advanced SSS products available. In contrast with previous datasets, the new products have global coverage and are generated for a 6-year period.
The new products are based in the debiased non-Bayesian method, as the previous ones. Some minors issues regarding the definition of the SMOS-based climatologies have been improved for the production of this new dataset.