Geophysical Research Abstracts
Vol. 15, EGU2013-7137, 2013
EGU General Assembly 2013
© Author(s) 2013. CC Attribution 3.0 License.
Synergy between ocean scalars: application to the improvement of SMOS
Antonio Turiel, Marta Umbert, Nina Hoareau, Justino Martínez, and Estrella Olmedo
Institute for Marine Sciences (ICM), Physical Oceanography, Barcelona, Spain (firstname.lastname@example.org)
Since the beginning of the satellite era it is well known that ocean scalars of different types can be used to identify
ocean structures. Not only that: on the identiﬁed structures the values of ocean variables exhibit some degree of
correlation. Although some schemes have been proposed to take advantage of these correlations, there has not
been a systematic exploitation of the redundancy among scalars.
The introduction of singularity analysis for remote sensing maps of the ocean has shown that the corre-
spondence among different scalars can be rigorously stated in terms of the correspondence of the values of their
associated singularity exponents. The singularity exponents of a scalar at a given point is a unitless measure of the
degree of regularity or irregularity of this function at that given point. Hence, singularity exponents can be directly
compared disregarding the physical meaning of the variable from which they were derived. Using singularity
analysis we can assess the quality of any scalar, as singularity exponents align in fronts following the streamlines
of the ﬂow, while noise breaks up the coherence of singularity fronts.
Taking the correspondence of the singularity exponents into account, it can be proved that two scalars hav-
ing the same singularity exponents have a relation of functional dependence (a matricial identity involving their
gradients). That functional relation can be approximated by a local linear regression under some hypothesis, which
simpliﬁes and speeds up the calculations and leads to a simple algorithm to reduce noise on a given ocean scalar
using another higher-quality variable as template.