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.

In this study, an experiment aimed to integrate Global Navigation Satellite System (GNSS) atmospheric data with meteorological data into a neural network system is performed. Precipitable Water Vapor (PWV) estimates derived from GNSS are combined with surface pressure, surface temperature and relative humidity obtained continuously from ground-based meteorological stations. The work aims to develop a methodology to forecast short-term intense rainfall. Hence, all the data is sampled at one hour interval.

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.

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is a limb-viewing infrared Fourier transform spectrometer that operated from 2002 to 2012 onboard the ENVISAT satellite. The maintenance and the upgrade of both L1 and L2 ESA processors are accomplished by the Quality Working Group, where a fruitful collaboration among Level 1, Level 2 and validation teams can be exploited. Recently both ESA L1 and L2 processors have been updated, as well as the spectroscopic database and some absorption cross-sections.

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).

Sleep Spindles are scalp EEG waveforms that are hallmarks of the N2 sleep stage and are believed to be generated through thalamo-cortical loops. In this study we set out to study the interaction between cortical and subcortical regions involved in spindle activity using non-invasive measurements.