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We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat “SmartLNB” (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge.

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In this paper an in-depth analysis on the performance of the Fourier analysis in estimating the first moment of Doppler spectra of rain signals from a spaceborne radar is presented. Spectral moments estimators based on Fourier analysis (DFT-SME) have been widely used by Doppler weather radars in measuring rainfall velocity and they have been found to be almost optimal for narrow normalized spectral widths (w N ). They are also more computationally efficient than the Maximum Likelihood estimators. However, the existing analytical approaches for evaluating the DFT-SME performance have mostly been focused on a limited range of small w N (e.g., w N < 0.1) that are typical of ground based and airborne Doppler weather radars. With the rapid advances in spaceborne radar technologies, the flying of a Doppler precipitation radar in space to acquire global data sets of vertical rainfall velocity has become a real possibility. The objective of this work is to develop a generalized analytical approach by extending it to cover larger values of w N (e.g., w N ~ 0.2) in spaceborne radar applications. In particular, a method has been developed to properly treat the aliasing effects, which have become a significant error source in spaceborne applications. Several DFT-SME algorithms (differing in the adopted strategy for noise handling and the initial estimate of the mean Doppler velocity) have been analyzed with this generalized approach. The analytical results are in excellent agreement with those obtained through simulation. Such encouraging results suggest that the proposed approach is a reliable technique for fast and accurate prediction of DFT-SME performance for spaceborne Doppler weather radars.