Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts.

The aim of this paper is to describe how ground-based radar interferometry can provide displacement measurements of earth dam surfaces and of vibration frequencies of its main concrete infrastructures. In many cases, dams were built many decades ago and, at that time, were not equipped with in situ sensors embedded in the structure when they were built. Earth dams have scattering properties similar to landslides for which the Ground-Based Synthetic Aperture Radar (GBSAR) technique has been so far extensively applied to study ground displacements.

We study the impact of assimilating very high-resolution Precipitable Water Vapor (PWV) maps into a non-hydrostatic Numerical Weather Prediction (NWP) model by the three-dimensional variational (3D-var) technique. PWV maps are obtained by processing the Sentinel-1 Synthetic Aperture Radar (SAR), using the SAR interferometry (InSAR) technique. Changes in the 3D distribution of water vapor, temperature and wind are studied to explain the onset of a deep convection phenomenon.

This paper is focused on visualization of the information extracted by GBSAR data acquired in landslide areas. It describes the way Graphical Processing Units (GPUs) can be used to generate and visualize accurate GBSAR images and displacement maps in near real time. Examples of GBSAR images, as radar coordinates and rendered on Digital Surface Models (DSMs), coherence and displacement maps are shown.

In this paper we analyze the influence of the geographical position on the increase of the total electron content in the ionospheric D-region during solar X-ray flares. We modeled the total electron content using data related to signals whose propagation paths lie in the mid and both mid and low latitude ionosphere. The obtained results indicate a larger increase of the total electron content in the perturbed equatorial D-region where the solar radiation is more pronounced and causes a larger electron density gradient with altitude.

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions.

In this note we prove a modular variable Orlicz inequality for the local maximal operator. This result generalizes several Orlicz and variable exponent modular inequalities that have appeared previously in the literature.

We present a Python open-source package called Visbrain that offers a coherent visualization suite for multi-modal brain data (intracranial and scalp EEG, MEG, structural and functional MRI). The current version of Visbrain is essentially articulated around four modules dedicated to 1) 3D visualization of functional and/or connectivity results (Brain), 2) polysomnographic data visualization and sleep analysis (Sleep, [1]), 3) data mining and basic plotting functions (Signal), 4) topographic representation (Topo).