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 firstname.lastname@example.org. 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.
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.
The Cryosphere, 11, 1987-2002, 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.
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.
We are pleased to inform you that our paper “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity” has recently appeared in Remote Sensing of Environment.
In the paper, we present a new method to process SMOS data in order to obtain more precise, less biased values of Sea Surface Salinity (SSS). With the new methodology, we do not only improve the overall quality of SSS data, but we also obtain valid retrievals in areas previously deemed as inaccessible, such as the Mediterranean.
Since last September, Remote Sensing Systems (REMSS) is producing version 2.0 of the Level 2 and Level 3 Sea Surface Salinity products from SMAP. One year ago, we published in this blog a brief study on the validation of version 1.0 of the 8-day L3 SSS maps provided by REMSS (see Preliminary validation of 8-day SMAP L3 Salinity product V1.0 for more information). Now, in order to assess the improvements of this new version, we present a small comparison between these two versions of the 8-day SSS L3 maps. Part of this study was included in the V2.0 Release Notes document. The validation has been made using as reference field the 7-day global ocean 0.25-degree SSS FOAM product generated by Met Office and distributed by Copernicus.