A blending algorithm using SST data to improve SMOS SSS maps

Data fusion is a process for combining two, or more, sources of information to improve the representation of a given system. In a recent paper, data fusion has been used to remove noise from SMOS sea surface salinity (SSS) products, by fusing SMOS data with sea surface temperature (SST) fields.

Our approach is justified by the correspondence between the singularity exponents of SSS and SST. The singularity exponent is a non-dimensional measure of the regularity or irregularity of a field in a given point. The value of the singularity exponent increases with the smoothness of a field. The correspondence between the singularity exponents of SST and SSS implies the existence of a local functional dependence between these two variables. This correspondence can be illustrated using data of a numerical simulation (OFES, Ocean General Circulation Model for the Earth Simulator).

Figure 1 shows two conditioned histograms. The one in the top illustrates the histogram of SSS conditioned by each given value of SST. The conditioned histogram looks like a superposition of narrow lines. It indicates that, while strong local SSS-SST correlations exist, these relations do change from one region to the other. On the contrary, the conditioned histogram of SSS singularity exponents conditioned by the value of the singularity exponents of SST indicates that a unique correlation exists all over the world ocean. In fact, the slope of the maximum probability line is close to one, indicating an almost perfect identity between the singularity exponents of SST and SSS.


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Data assimilation of SMOS SSS data to create Level 4 salinity maps. A case study

Many approaches can be used to reduce the amount of noise present in a given set of data (observed or retrieved). In the SMOS processing chain, weighted averages are used to reduce the noise present in the sea surface salinity (SSS) data retrieved from brightness temperature measurements. This is the rationale of the existence of the higher production levels (Levels 3 and 4) of sea surface salinity and soil moisture.

Differences between SMOS level 3 (right) or FREE-run(left) and Argo data (2011). All 2011 match ups are shown in these plots.

Figure 1: Differences between SMOS level 3 (right) or FREE-run(left) and Argo data (2011). All 2011 match ups are shown in these plots.

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New ocean products available!

Since March 5th 2013, SMOS-BEC distributes new level 3 and level 4 products derived from level 2 data processed by ESA

The SMOS data used to compute the new level 3 and level 4 Ocean products come from level 2 (L2) Ocean Salinity User Data Product (UDP) and Ocean Salinity Data Analysis Product (DAP). These UDP and DAP files are generated by ESA and include geophysical parameters, a theoretical estimate of their accuracy, and flags and descriptors for the product quality for three different roughness models. These new products developed by BEC are based on the roughness model described in Guimbard et al IEEETrans. Geeosci. Remote Sens. (2012).

What’s new?

The new maps have been created using an improved filtering technique over L2 products. Also the resolution has been modified: current maps are generated at 0.25 degree resolution (rather than 1 degree resolution as for the previous products). The variety of averaging periods has also been increased: three days, nine days (generated every three days), monthly, seasonal, and annual averages (see SMOS-BEC Ocean and Land Products Description for additional information) are now available.

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Research SSS products with improved resolution

All ocean L4 products distributed by CP34 BEC are obtained by the application of singularity-based fusion. We will discuss this technique in greater detail in this blog when the paper presently under revision is available. So far, it suffices to comment that with this technique a template variable (Sea surface temperature, SST, in our case) of good quality is used to restore the multifractal structure of singularity fronts on a noisy variable (SSS in our case). To know more about the multifractal structure of ocean scalars please consult the 2009 Ocean Science paper.

Sequence of binned L3 SSS maps

The animation above represents the sequence of binned L3 SSS maps; each frame is a 10-day average, which a time lag of three days between the beginning of consecutive averaging periods. This map has a resolution of 1 degree X 1 degree, what is a rather coarse time and space resolution when phenomena like Tropical Instability Waves or the onset of a El Nino are sought. To make things worse, present levels of accuracy on SMOS products make even harder to characterize this large scale phenomena. This is a typical situation in which L4 products can come to rescue!

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Christmas, El Niño, and SMOS

Christmas is surely an appropriate moment to talk about the “El Niño / Southern Oscillation” (ENSO). Today there is no doubt that ENSO is the largest source of inter-annual climate variability at regional and planetary scales. Although its ocean-atmosphere coupled nature was postulated in 1969, the quasi-periodic oceanic and atmospheric anomalous behavior has been observed for centuries. For more than five hundred years, Peruvian fishermen and farmers have been aware that a periodic warm surface counter-current off the Peruvian Coast reduces the anchovy catch, while, at the same time, increased rainfalls transform barren lands onto fertile ones. This counter-current was termed as the current of the “El Niño” (the Child Jesus) because it usually appears around Christmas. On the other hand, several tens of thousands of kilometers to the west, over the Asian continent, other climate events also have a strong impact on society. For example, the failure of monsoons resulted in the Great Drought (1876-1877) that contributed to cause more than seven million deaths in the British-controlled India. Since then, various efforts were made to predict the interannual variability of the Indian monsoons. In 1904 Sir Gilbert Walker was appointed as the director-general of Observatories in India to lead such task. Although Walker was not aware of the El Niño current, he did know about the existence of synchronized interannual pressure fluctuations over the Indian Ocean and eastern tropical Pacific (fluctuations that Walker called the “Southern Oscillation”). His research team evidenced that monsoons are part of a global phenomenon, and that the Southern Oscillation is correlated with major changes in the rainfall patterns and wind vents over the tropical Pacific and Indian Oceans.

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