SMAP SSS provided by REMSS: v1.0 vs v2.0

Outliers distribution is very homogeous in both versions

Fig. 1: Outliers distribution (red dots) is homogeneous in both versions. The nearest points to the coast are also excluded from statistics.


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.

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Preliminary validation of 8-day SMAP L3 Salinity product V1.0

Atlantic discharges. April 25-May 2, 2015

Amazon, Niger and Congo discharges over the Atlantic Ocean as measured by SMAP

Scientists at Remote Sensing Systems (RSS, http://www.remss.com), using the experience acquired with the Aquarius mission are developing the necessary algorithms to retrieve sea surface salinity from brightness temperature provided by the SMAP radiometer team.

Recently, RSS has released version 1.0 (BETA) SMAP Level 3 Ocean Surface Salinities. The data can be accessed through the RSS web site or FTP server and it is described in [Meissner et al., 2015]. Their Level 3 salinity product has worldwide coverage and correspond to  8-day and monthly averages. The 8-day average field, centered on each day, starts on April 4, 2015 and ends at November 15, 2015.

A preliminary comparison of the 8-day L3 product with ARGO profiles and the World Ocean Atlas (WOA13) climatology has been performed by BEC team over the zones indicated on the map below.

Zones under study

Zones under study (click to enlarge image)

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Preliminary SMOS SSS in the Mediterranean

Experimental SMOS SSS maps of the Mediterranean Sea are being computed at BEC using a new methodological approach to cope with land and RFI contamination. Three different products are being analysed: monthly binned maps at a 1×1 deg grid; optimal interpolated maps at 0.25×0.25 deg; and daily products at 0.25×0.25 deg through fusion with Reynolds SST.  The preliminary assessment of the monthly product shows an RMS with respect to ARGO of 0.35 psu. These maps will be available soon in our CP34-BEC data distribution system, so keep watching!
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New service available: Singularity Analysis

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

Microwave OI SST map (AMSRE-E+TMI, derived by Remote Sensing Systems) corresponding to January 1st, 2005

Map of associated singularity exponents

Map of associated singularity exponents

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Global Land products available!

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

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


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New reprocessed ocean products

zones

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).

STD 9D binned

Fig 2: Standard deviation of SMOS minus ARGO SSS differences in 9-day binned maps for different ocean regions

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New authentication method

A new authentication method has been implemented to access the SMOS data generated at BEC. Until now, all data users had the same username and password. From now on, every user will have her/his own username and password. This new implementation requires a personal re-registration of the current users. This procedure is necessary in order to properly manage the amount of users and future services.

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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.

Figure1

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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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