Pyramidal rain field decomposition using Radial Basis Function neural networks for tracking and forecasting purposes

In this paper, we present how we used neural networks (NNs) and a pyramidal approach to model the data obtained by a weather radar and to short-range forecast the rainfall behavior. Very short-range forecast is useful, for instance, for estimating the path attenuation in terrestrial point-to-point communications. Radial basis function NNs are used both to approximate the rain field and to forecast the parameters of this approximation in order to anticipate the movements and changes in geometric characteristics of significant meteorological structures. The procedure is validated by applying it to actual weather radar data and comparing the outcome with a linear forecasting method, the steady-state method, and the persistence method. The same approach is probably useful also for predicting the behavior of other meteorological phenomena like clusters of clouds observed from satellites

Preparing an urban test site for SRTM data validation

In this paper, we describe a method to obtain a reliable set of elevation data suitable for data validation on the Shuttle Radar Topography Mission (SRTM), starting from laser scanning measurements on an urban test site: Pavia, Northern Italy. The elevation dataset is obtained through extraction of digital terrain models. The source digital surface model is first filtered by means of a lowpass or morphological kernel. Then, buildings are suppressed through analysis of the height histogram. Finally, a lowpass filter suppresses the surviving elevation artifacts. We show that, starting from a digital surface model at 1-m ground resolution, we end up with a digital terrain model that can be used as a ground truth for SRTM topographic analysis of an urban area