Remote sensing imagery of the ocean surface provides a synoptic view of mesoscale signatures from different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of the oceanic flow. At spatial scales of kilometers, turbulence is regarded as a two-dimensional phenomenon, with a complex geometry. Such complexity emerges in remote sensing images as filaments and eddies of different sizes. This is seen in images of surface chlorophyll-a concentration (Chl-a) and sea surface salinity (SSS), as well as the betterresolved sea surface temperature (SST) and sea surface height (SSH). A fusion technique has been recently proposed to exploit these common turbulent signatures among the different variables. This technique is based on the hypothesis that the spatial structure of a tracer inherits some properties of the underlying flow [Turiel et al., 2005]. This yields an organized geometry of the flow as a hierarchy of fractal structures, called singularity manifolds, each of them associated with a singularity exponent. This is the so-called multifractal formalism for fully developed turbulence [Lovejoy et al., 2001]. Such geometrical arrangement of the flow is intimately linked to the energy cascade. By assuming that the singularity lines of the different ocean variables coincide,, the gradient of two variables should be related through a smooth function [Umbert et al., 2013]. In a first approach, the function is expressed as the identity, leading to a local regression scheme. When applied to SMOS SSS data, this simple approach already allows a significant noise reduction and coverage improvement of the resulting Level 4 product using OSTIA SST fields as a template [Umbert et al., 2013]. Moreover, information about the spatial relationship between the two fields can also be obtained. This methodology is now applied to daily Aqua MODIS Level-3 chlorophyll maps using MODIS SST maps as template, and to Aquarius SSS using SSH from AVISO as template. The resulting Chl-a and SSS maps contain the mesoscale structures seen in SST and SSH maps, exhibit a significant reduction of the uncertainty, and allow extrapolation to cloud-affected areas. This technique sets the grounds for reprocessing long time series of remote sensing derived parameters, by exploiting the information content of each variable to obtain spatial and temporally consistent datasets while preserving the turbulent energy cascade. Since this approach does not require the use of any background information (e.g., numerical models) can be used to improve ocean climate data records

The Climate Symposium 2014, 13-17 October 2014, Darmstadt, Germany.-- 1 page

Peer Reviewed