In this paper the Acoustic Analogy is used to predict the underwater noise from a complete scaled ship model in a steady course. The numerical investigation is performed by coupling an incompressible RANS code, equipped with a level-set approach to account for the fundamental time evolution of the free surface, to a FWHbased hydroacoustic solver, here suitably designed to manage the huge set of data coming from a full-unsteady hydrodynamic simulation.

The acoustic analogy represents a powerful and versatile approach, able to numerically predict the noise generated by a body moving in a fluid. It is widely used to provide essential indications about the aeroacoustic behavior of aircraft and helicopters (even at a design stage) and, eventually, to pursue effective strategies aimed at desirable reduction and/or control of noise. Nevertheless, applications in the area of hydroacoustics and in the prediction of ship underwater noise are very rare.

An algorithm for computing the solution of indefinite least squares problems and of indefinite least squares problems with equality constrained is presented. Such problems arise when solving total least squares problems and in H infinity-smoothing. The proposed algorithm relies only on stable orthogonal transformations reducing recursively the associated augmented matrix to proper block anti-triangular form. Some numerical results are reported showing the properties of the algorithm.

This paper investigates the use of time series of ALOS/PALSAR-1 and COSMO-SkyMed data for the soil moisture retrieval (mv) by means of the SMOSAR algorithm. The application context is the exploitation of mv maps at a moderate spatial and temporal resolution for improving flood/drought monitoring at regional scale. The SAR data were acquired over the Capitanata plain in Southern Italy, over which ground campaigns were carried out in 2007, 2010 and 2011.

In this paper we introduce new variables that can be used to retrieve the atmospheric continuum emission in the inversion of remote sensing measurements. This modification tackles the so-called sloppy model problem. We test this approach on an extensive set of real measurements from the Michelson Interferometer for Passive Atmospheric Sounding. The newly introduced variables permit to achieve a more stable inversion and a smaller value of the minimum of the cost function.