Maximum covariance analysis of the sea surface backscatter signal models

In this work we study the applicability of the maximum covariance analysis (MCA) for the analysis of matrices characterizing the spatiotemporal models of sea surface backscatter signals for different types of sea waves. The method is based on the singular value decomposition of the covariance matrix describing the relationship between two spatiotemporal matrices. The dependence of the obtained correlation coefficients on the degree of sea roughness, as well as on the ratio of the heights of wind waves and rogue waves are determined. The statistical characteristics of the obtained correlation coefficients of the sea surface backscatter signals are analysed. Our results indicate that the MCA method, at least from the modelling perspective, could be applicable to the classification of the sea surface from its backscatter signal characteristics, including an early detection and analysis of the rogue waves onset and development.

CORRELATIVE AND COVARIANCE ANALYSIS OF THE ELECTROCONDUCTIVE FABRICS

This paper presents the correlation and covariance analysis of the electrical resistance dependence of mass, thickness and air permeability of textile structures coated with magneto-conductive materials and used for protection against electromagnetic waves. For this scientific approach, 9 experimental models of compositesbased fabrics with electroconductive properties were made by applying different pastes based on organic polymeric matrices polyvinyl alcohol (PVA), copper (Cu), aluminium (Al), zinc (Zn), iron oxide (Fe3O4) and polypyrrole (PPY) solution using a classical deposition method such as scraping. The surface resistance was evaluated using a resistance meter device based on two parallel electrodes. From all samples analysed 5 samples based PPY, PVA-Ni, PVA–Ni-Al and PVA-Zn present a surface resistance between 103 and 105 Ω. Because the surface resistance has a lower value this means that the samples have a good conductivity to be used as layers for electromagnetic shielding systems.

Lagged teleconnections of climate variables identified via complex rotated Maximum Covariance Analysis

A proper description of ocean-atmosphere interactions is key for a correct understanding of climate evolution. The interplay among the different variables acting over the climate is complex, often leading to correlations across long spatial distances (teleconnections). In some occasions, those teleconnections occur with quite significant temporal shifts that are fundamental for the understanding of the underlying phenomena but which are poorly captured by standard methods. Applying orthogonal decomposition such as Maximum Covariance Analysis (MCA) to geophysical data sets allows to extract common dominant patterns between two different variables, but generally suffers from (i) the non-physical orthogonal constraint as well as (ii) the consideration of simple correlations, whereby temporally offset signals are not detected. Here we propose an extension, complex rotated MCA, to address both limitations. We transform our signals using the Hilbert transform and perform the orthogonal decomposition in complex space, allowing us to correctly correlate out-of-phase signals. Subsequent Varimax rotation removes the orthogonal constraints, leading to more physically meaningful modes of geophysical variability. As an example of application, we have employed this method on sea surface temperature and continental precipitation; our method successfully captures the temporal and spatial interactions between these two variables, namely for (i) the seasonal cycle, (ii) canonical ENSO, (iii) the global warming trend, (iv) the Pacific Decadal Oscillation, (v) ENSO Modoki and finally (vi) the Atlantic Meridional Mode. The complex rotated modes of MCA provide information on the regional amplitude, and under certain conditions, the regional time lag between changes on ocean temperature and land precipitation.