Real-time precipitation maps from satellite broadcast signals

Broadcast satellite telecommunication bring information on rainfall rates along their path, through the signal attenuation, caused by raindrops. We exploited this feature with new two-way (transmit-receive) devices named SmartLNB (Smart Low-Noise Block converter), that are going to constitute networks of sensors of opportunity, in any urbanised areas. Albeit not specifically developed for meteorology, two-way receivers centralise attenuation data, ready to be processed for rainfall estimation, every minute. The high rate of real-time measurements provided by SmartLNBs suggested approaching the rainfall retrieval problem as a trajectory assessment in a phase space, using an ensemble Kalman filter, to produce rainfall fields over a given spatial domain. SmartLNBs provide average measurements along quasi-parallel non-nadir satellite-receiver paths, so that information on the structure of the intercepted rainfall system have to be known to retrieve ground precipitation. MSG satellite observations can be used at the purpose and as initial and boundary conditions, while atmospheric motion vectors from the same data source are used in the propagation model of the Kalman filter. A cross-shaped experimental network of a dozen SmartLNBs has been deployed in Florence (Italy) with co-located raingauges and one doppler polarimetric X-band radar for cal/val objectives. In this work, we present the measurement concept, the signal processing algorithm, the method to estimate the rainfall fields and the results, notable over the expectations. Firstly, some significant synthetic case studies are illustrated, for various precipitative events with different intensity, dynamics and morphology and for various sensor distributions. Then real measurement retrievals (single and collective) from the project network are shown and results discussed, with attention to the sensitivity of the technique to receiver density, satellite link geometry and rainfall systems characteristics.