The Gulf Stream plays a major role in the meridional transport of heat and salt across the North Atlantic Ocean. The Gulf Stream acts as a barrier between the cold (10-18 °C) and relatively fresh (salinity around 30-32 in the practical salinity scale) waters of the Labrador Current and the warm (23 °C), salty (36), clear, and unproductive waters of the Sargasso Sea. After leaving Cape Hatteras, the Gulf Stream forms large-amplitude meanders that may loop back onto themselves and break off the stream forming detached rings. Warm-core anti-cyclonic rings bring significant amounts of warm tropical water to the continental slope and shelf seas north of the Gulf Stream. Similarly, cold-core cyclonic rings bring cold, nutrient-rich shelf water, to the biologically barren Sargasso Sea waters. Detection of cold-core rings from satellite data has been quite elusive so far as the surface temperature signature rapidly disappears.
In a recent study by M. Umbert, the bivariate data fusion technique developped by (Umbert et al. 2014), which exploits the hypothesis that the singularity exponents of sea surface temperature must correspond to the ones of sea surface salinity, is used to both improve the quality of binned maps of remotely sensed SSS and to assess whether the salinity signature of mesoscale rings associated the Gulf Stream and Agulhas western boundary currents can be represented.
The Barcelona World Race Ocean Campus has organised five courses on Instructure Canvas platform to provide the 2014-2015 round the world regatta followers with basic knowledge about the science of oceanography and other subjects like meteorology, telemedecine, chronobiology or nutrition. One of these MOOCs is “Oceanography: a key to a better understanding of our world” that includes a module on ocean remote sensing instructed by Jordi Font. This free course will start on April 20th and does not require previous knowledge in oceanography. Feel free to join us in this worldwide adventure!
On January 31st, NASA successfully launched the SMAP satellite onboard a United Launch Alliance Delta II rocket. The satellite, designed to collect high resolution soil moisture maps on a global scale every two to three days, will improve the ability to forecast droughts, forest fires and floods, and will help in crop planning and rotation. On February 24th the reflector antenna was successfully deployed and in the following days the first radiometric data have been acquired.
Image: NASA, United Launch Alliance
In order to obtain detailed soil moisture measurements of the entire world, SMAP is placed in a near-polar sun-synchronous orbit, allowing the observatory to use Earth’s natural spin to maximize the area that can be scanned by the satellite’s instruments. The orbiter will use its L-band radar and L-band radiometer to scan the top 2 inches (5 cm) of our planet’s soil with a resolution of around 31 miles (50 km).
Since the beginning of SMOS mission, one of the problems that has strongly affected the quality of the retrieval of SSS from SMOS Brightness Temperatures (BT) is the presence of large human-generated Radio Frequency Interference (RFI) sources, as shown in the following figure:
Image acquired over a coastal area in Europe; several strong RFI sources and the associated tails are very noticeable
Radio Science has recently published “Microwave interferometric radiometry in remote sensing: An invited historical review” by M. Martín-Neira, D. M. LeVine, Y. Kerr, N. Skou, M. Peichl, A. Camps, I. Corbella, M. Hallikainen, J. Font, J. Wu, S. Mecklenburg, and M. Drusch. The paper (Radio Science, volume 49, issue 6, pages 415–449, June 2014, DOI: 10.1002/2013RS005230) is led by Manuel Martín-Neira, the SMOS instrument (MIRAS) principal engineer, and is co-authored by three SMOS-BEC members: Adriano Camps, Ignasi Corbella and Jordi Font. We copy below the paper’s abstract:
The launch of the Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed, thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d’Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnológico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.
This image of the first ESA ground tests of a MIRAS demonstrator was selected for the cover of the Radio Science issue. The online version of the paper can be seen at http://onlinelibrary.wiley.com/doi/10.1002/2013RS005230/full
From July 21 to 26, SMOS-BEC host at ICM the 17th meeting of the Ocean Observation Panel for Climate (OOPC) and the 3rd meeting of the Global Ocean Observing System (GOOS). The mission of OOPC is to develop recommendations for a sustained global observation of the oceans in relation to climate, while GOOS is a permanent global system for observations, modeling and analysis of marine and ocean variables to support operational ocean services worldwide. GOOS provides accurate descriptions of the present state of the oceans, including living resources; continuous forecasts of the future conditions of the sea as far ahead as possible, and the basis for forecasts of climate change. GOOS is made of many observation platforms including 3000 Argo floats, 1250 drifting buoys, 350 embarked systems on commercial or cruising yachts, 100 research vessels, 200 marigraphs, and more than 200 moorings in open sea.
Maybe you have seen the singularity exponents maps we are offering in this CP34-BEC data server. Singularity analysis is a technique for estimating, at any point, the singularity exponent of a signal. Singularity exponents, usually denoted by h, are dimensionless variables providing information about the local regularity (if positive) or irregularity (if negative) of the signal at any given point. When h is integer it means that the function has h continuous derivatives, while non-integer values indicate a more complex topological situation.
Why should we be interested in such a mathematical, abstract concept? Because if a flow exhibits horizontal turbulence – and the ocean is a quasi-2D turbulent flow at scales greater that a few kilometers – singularity exponents derived from any ocean scalar are the same and, in fact, they represent the streamlines of the flow! (Turiel et al., Physical Review Letters, 2005; Isern-Fontanet et al, Journal of Geophysical Research, 2007; Nieves et al, Geophysical Research Letters, 2007; Turiel et al., Remote Sensing of Environment, 2008; Turiel et al., Ocean Science, 2009).
Microwave OI SST map (AMSRE-E+TMI, derived by Remote Sensing Systems) corresponding to January 1st, 2005
Map of associated singularity exponents
Passive microwave remote sensing at L-band is considered to be the most suitable technique to measure soil moisture and ocean salinity from space. The ESA’s SMOS and the NASA’s Aquarius/SAC-D are the two first satellite missions, carrying L-band radiometers on-board, measuring the global Earth’s surface as brightness temperatures (TB). The two radiometers have important differences in the architecture of the instruments as well as in their operation principles. In order to verify the continuity and the consistency of the data over the entire dynamic range of observations, a comparison between one year of SMOS and Aquarius measured TB has been performed over key regions over land (Amazon rainforest and Sahara desert), ice (Dome-C in Antarctica) and sea (South Pacific ocean).
Click here to observe selected regions in Google Earth.
A global view of the comparison is shown in Fig. 1, which displays the annual mean of the two radiometers for the three Aquarius incidence angles (inner 29.36º, middle 38.49º and outer 46.29º beams). In South Pacific, Dome-C and Sahara, higher incidence angles imply lower TB at horizontal polarization and higher TB at vertical polarization. However, in the Amazon, the TB variation with incidence angle and polarization is not clear due to the vegetation scattering. As expected, there is a small difference between polarizations (TBV-TBH) for vegetation-covered soils.
Fig. 1 Aquarius TB vs. SMOS TB at (a) horizontal and (b) vertical polarizations.
A Soil Moisture (SM) Level 3 product has been created at BEC, and it is now available online.
The Level 3 product is generated from the operational ESA Level 2 Soil Moisture User Data Product (UDP) that include geophysical parameters, a theoretical estimate of their accuracy, and a set of product flags and descriptors.
The nominal L2 SM data is first filtered in order to ensure the quality of our L3 products. Soil Moisture values are rejected if: i) no value has been retrieved for that given gridpoint; ii) the retrieval is negative; iii) the retrieval is outside the extended range; or iv) the associated Data Quality Index (DQX) is larger than 0.07 m³/m³ . Next, a weighted average is performed to bin the data to a EASE-ML grid with cells of 25 km (see documentation for additional information). Products are provided in netcdf format.
SMOS soil moisture L3-days binned maps. The plots show the soil moisture evolution during the Bosnian floods in May 2014. Heavy rains was received from 14 to 16 of May 2014
Fig 1: Zones under study in figures 2-4
New reprocessed Sea Surface Salinity products at 0.25 degrees grid spacing are available online. A complete set of products (weighted averaged, optimally interpolated and fused maps) corresponding to the year 2013 has been generated. With the reprocessing of these data, BEC provides the SMOS users with a uniform set of SSS maps for most of the current operating life of SMOS (period 2010-2013).
Fig 2: Standard deviation of SMOS minus ARGO SSS differences in 9-day binned maps for different ocean regions