New SMOS Sea Ice Concentration products

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 [1]. 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 [1] 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.

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Please, do not hesitate to contact us in case you have any question or comment at smos-bec@icm.csic.es. Your feedback is most welcome!

Enjoy the products!

BEC team

[1] 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/

Six years of the new SMOS SSS maps in the Mediterranean Sea now available!

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).
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Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

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)

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)

 

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

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.

figura_SIC_2

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.

https://www.the-cryosphere.net/11/1987/2017/

The Cryosphere, 11, 1987-2002, 2017
https://doi.org/10.5194/tc-11-1987-2017

 

BEC joins public condemnation of the use of violence against Catalan people

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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.

Visca Catalunya!

The BEC team

Celebration of BEC 10th Anniversary

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.

20170619_10años BEC (58)

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Advanced SSS products now available with global coverage!

Objectively Analysed SSS for the period May 27th to June 4th, 2014

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.

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Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity

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.

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Preliminary SWDI maps using the BEC L4 soil moisture product

The Water Resources Research Group of the University of Salamanca has developed a new agricultural drought index, the so-called Soil Water Deficit Index (SWDI) [1], [2], based in soil moisture and soil parameters. Using the high resolution BEC L4 soil moisture product [3] as an input of the SWDI, agricultural drought maps of Zamora province (west of Spain) were derived (Fig. 1). With this product, agricultural drought conditions in the most important agricultural regions in Spain will be monitored.

The results of this research will be published soon, so stay tuned!

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Fig.1. SWDI-SMOS map at 1 km spatial resolution of Zamora province showing wet (02/12/2010, Up) and dry (24/08/2011, Down) conditions.

Fig.1. SWDI-SMOS map at 1 km spatial resolution of Zamora province showing wet (02/12/2010, Up) and dry (24/08/2011, Down) conditions.

[1] Martínez-Fernández, J., González-Zamora, A., Sánchez, N., & Gumuzzio, A. (2015). “A soil water based index as a suitable agricultural drought indicator.” Journal of Hydrology, 522, 265-273.

[2] Martínez-Fernández, J., González-Zamora, A., Sánchez, N., Gumuzzio, A., & Herrero-Jiménez, C.M. (2016). “Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index.” Remote Sensing of Environment, 177, 277-286.

[3] Piles, M., Camps, A., Vall-llossera, M., Corbella, I., Panciera, R., Rüdiger, C., Kerr, Y.H., & Walker, J. (2011). “Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data.” IEEE Transactions on Geoscience and Remote Sensing, 49, 3156-3166.

A big tour sampling the North Atlantic ocean

 

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In march 2013 an international experiment, the Salinity Processes in the Upper ocean Regional Study (SPURS), was carried out with the goal of performing a wide range of mesoscale and submesoscale measurements to understand the mechanisms of formation and permanence of the largest ocean salinity maximum in the centre of the North Atlantic subtropical gyre. Several standard and prototype instruments were used in measuring the Sea Surface Salinity (SSS) and other ocean variables. Among many activities developed during the SPURS-MIDAS cruise, the ICM contribution to SPURS, a set of new Lagrangian drifters to measure the SST and SSS were deployed. These were part of a total set of 114 similar drifters deployed during the whole experiment (Centurioni et al, 2015). Now almost three years later, three of these units are still providing data after performing a big tour around the North Atlantic.

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