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First results on the performance of ARIEL radiometer in MOSAIC Expedition

ICE-MOD – MOSAIC Project, Programación conjunta internacional

The aim of this project is to improve the quality of the emissivity models and the dielectric constant model (permittivity) of the sea ice at 1.4 GHz (L-band). They will be used in the inversion algorithms for retrieving key sea ice parameters, as for example sea ice thickness and snow depth from the space borne L-band microwave radiometers.

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New release of 1-km SMOS soil moisture over European and Mediterranean countries

Dear users,

First, we hope that you and your family are doing well in these challenging days.

We are pleased to announce the latest version (v5.0) of cloud-free BEC SMOS (Soil Moisture and Ocean Salinity) L4 soil moisture maps at 1 km covering Europe, the northern-most countries of Africa and western Russia. These maps are obtained from the synergy of:

  • SMOS L1C brightness temperature,
  • Terra MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index), and
  • ECMWF (European Center for Medium Weather Forecast) skin temperature.

The use of an adaptive window allows the downscaling algorithm to be applied over any non-frozen region of the world, regardless of the size of the region to be processed, and its spatial climatic variability. The BEC SMOS L3 soil moisture is used as a benchmark.

As a novelty, daily and 3-day L4 soil moisture maps are distributed in two different modes:

  • Near Real-Time maps provided with a latency of only 2 days,
  • Reprocessed maps provided with a latency of 3-4 weeks.

The reprocessed mode contains the series of all maps since June 2010.

Additionally, nomenclature and metadata of the latest version (v3.0) of global SMOS L3 soil moisture have been revised and updated.

A detailed explanation of both L3 and L4 algorithms and the resulting products is included in BEC SMOS Soil Moissture Products Description. 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!

The BEC team

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New release of Global SMOS L3 and L4 Sea Surface Salinity

Dear users,

First, we hope that you and your family are doing well in these challenging days.

We are pleased to release the latest version of the global BEC L3 and L4 SSS products. The new time series comprises 9 years (2011-2019). Some of the improvements of this product with respect to the previous versions are:

i) latitudinal and seasonal bias has been reduced; 

ii) new filtering criteria have been applied to be more consistent with the geophysical signal;

iii) an estimate of the salinity uncertainty is provided in the L3 product.

A detailed explanation of the algorithm and the performance of the new products when comparing with Argo floats are included in BEC Global SSS Product Description BEC Global SSS Products Description .

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!

The BEC team
http://bec.icm.csic.es

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New release of Europe SMOS L4 Soil Moisture at 1 km

We are pleased to release the latest version of the cloud-free BEC L4 soil moisture at 1 km over Europe. The L4 soil moisture v4 maps are produced from the synergy of:

  • SMOS L1C brightness temperature,
  • European Center for Medium Weather Forecast (ECMWF) land surface temperature (LST),
  • Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI).

The downscaling technique uses the BEC L3 soil moisture v3 as benchmark.
A detailed explanation of the downscaling algorithm and the resulting product is included in BEC Land Products Description

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SSS Arctic product version 2 is available

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 It is available through our FTP service.

Discharge of Mackenzie river captured by BEC Arctic SSS product

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 ).

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Important changes in the distribution of BEC products

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).

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New release of global SMOS soil moisture products at BEC

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.

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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.

figura_SIC

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/

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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).
SMOS_Mediterranena_20121001_20121231
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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)