We use high-resolution direct numerical simulations to study the anisotropic contents of a turbulent, statistically homogeneous flow with random transitions among multiple energy containing states. We decompose the velocity correlation functions on different sectors of the three-dimensional group of rotations, SO(3), using a high-precision quadrature. Scaling properties of anisotropic components of longitudinal and transverse velocity fluctuations are accurately measured at changing Reynolds numbers.

The Stokes drag force and the gravity force are usually sufficient to describe the behavior of sub-Kolmogorovsize (or pointlike) heavy particles in turbulence, in particular when the particle-to-fluid density ratio rho(p)/rho(integral) greater than or similar to 10(3) (with rho(p) and rho(f) the particle and fluid density, respectively). This is, in general, not the case for smaller particle-to-fluid density ratios, in particular not for rho(p)/rho(f) greater than or similar to 10(2).

Anisotropic particles are present in many natural and industrial flows. Here we perform direct numerical simulation (DNS) of turbulent pipe flows with dispersed finite-size prolate spheroids simulated by means of the lattice Boltzmann method (LBM). We consider three different particle shapes: spheroidal (aspect ratio 2 and 3) and spherical. These three simulations are complemented with a reference simulation of a single-phase flow. For the sake of comparison, all simulations, laden or unladen have the same energy input.

In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms.

To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas.

A Global Navigational Satellite System (GNSS) tomography system is implemented in the Lisbon area, Portugal, to estimate the water vapor dynamics at a local scale. A field experiment was carried out, in which a series of temporary GNSS stations were installed, increasing the network from 9 permanent stations to a total of 17 GNSS stations. A radiosonde campaign was also performed with high sampling launches, at 4-h intervals, for 1 week. A time series of hourly 3D wet refractivity solutions were obtained during the radiosonde campaign.

In this paper we summarize the results of an experiment aiming to compare soil moisture estimates obtained by Sentinel-l interferometric data with in-situ measurements. The study area, located close to Lisbon in Companhia das Lezirias, Portugal is characterized by a flat topography, large agricultural areas and sparse vegetation. In a test site, four soil moisture sensors were deployed and soil moisture was measured (at a depth of 5 cm) for a period of 7 months in an hourly basis.