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 in our website.
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 ).
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.
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 (click to enlarge image)
The SMOS Mediterranean SSS products announced in a previous post have now been made available at our webpage. A preliminary quality report can be accessed here.
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!
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
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
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.
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.