The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals

TL;DR

This paper introduces the NEFOCAST system that detects and estimates rainfall by analyzing satellite signal attenuation, employing Kalman filters to distinguish rain effects from other propagation disturbances.

Contribution

It presents a novel approach using opportunistic satellite signals and advanced filtering techniques for rainfall detection and estimation.

Findings

Effective detection of rain events from satellite signals.

Accurate estimation of rainfall rates using Kalman filters.

Robustness against propagation disturbances and orbit perturbations.

Abstract

In this paper we present results from the NEFOCAST project, funded by the Tuscany Region, aiming at detecting and estimating rainfall fields from the opportunistic use of the rain-induced excess attenuation incurred in the downlink channel by a commercial DVB satellite signal. The attenuation is estimated by reverse-engineering the effects of the various propagation phenomena affecting the received signal, among which, in first place, the perturbations factors affecting geostationary orbits, such as the gravitational attraction from the moon and the sun and the inhomogeneity in Earth mass distribution and, secondly, the small-scale irregularities in the atmospheric refractive index, causing rapid fluctuations in signal amplitude. The latter impairments, in particular, even if periodically counteracted by correction maneuvers, may give rise to significant departures of the actual satellite position from the nominal orbit. A further problem to deal with is the daily and seasonal random fluctuation of the rain height and altitude/size of the associated melting layer. All of the above issues lead to non-negligible random deviations from the dry nominal downlink attenuation, that can be misinterpreted as rain events. In this paper we show how to counteract these issues by employing two differentially-configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, so that the rain events can be reliably detected and the relevant rainfall rate estimated.