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Modern ways to measure rainfall provide a variety of different solutions, direct and indirect, with respect to the standard approach that is the raingauge method. Retrieving the actual rain fallen on a target domain is not in fact as easy task due to its temporal and spatial variability, but its importance is paramount for meteorology and for the effects on human lives and the environment. Rainfall regimes are changing almost at every latitude with dramatic effects, with a complex connection to climate change in large part to be still understood.Among the emerging new methods for rainfall estimation, a specific interest is in the so-called ‘opportunistic’ measurements, because they provide a chance to augment information without adding new infrastructures, also with clear cost advantages. These data are of course less precise than those from dedicated instruments. Therefore some smart efforts in devising proper processing are needed to extract all the geophysical information that they can provide. Use of microwave links in cellular phone networks is among these methods, bringing information on rainfall rates along their path through signal attenuation caused by raindrops. Following a similar principle also broadcast telecommunication satellite signals can be used, with additional problems related to the definition of the intercepted precipitation volumes and the effects of the melting layer, but additional advantages related to the worldwide availability of the signal and the easiness of data acquisition, that can be natively centralised when using two ways communication receivers. NEFOCAST, a research project funded by the regional administration of Tuscany (Italy), exploited this feature through new two-way (transmit-receive) devices named SmartLNB (Smart Low-Noise Block converter), that are going to constitute networks of sensors of opportunity, densely distributed especially in urbanised areas. Two-way receivers allow both to estimate and relying attenuation data that can be centralized to be processed for real-time rainfall estimation, every minute.An experimental network of SmartLNBs has been deployed in Italy (namely Florence, Pisa and Rome), including co-located raingauges and radar measurements for cal/val objectives. SmartLNBs provide average measurements along quasi-parallel non-nadir paths, so that information on the structure of the intercepted rainfall system is needed in order to retrieve ground precipitation. The high rate of measurements provided by the SmartLNBs suggested to approach the rainfall retrieval problem similarly to a trajectory assessment in a phase space, using an ensemble Kalman filter to produce the rainfall field over a given domain. MSG satellite precipitation products can be used for the purpose and also as initial and boundary conditions, while atmospheric motion vectors from the same data source are used in the propagation model of the Kalman filter.In this work, we present the measurement concept, the signal processing algorithm and the method to retrieve the rainfall fields, through some significant synthetic and real case studies, for events with different intensity, dynamics and morphology and for various sensor distributions.

Accurate measurement and monitoring of precipitation is crucial for many applications, such as flood and drought risk assessment and management. Conventional meteorological devices for estimating precipitation (i.e., rain gauges, disdrometers, active and passive remote sensors, be they ground-based, spaceborne, or airborne) have their own strengths and weaknesses. The latter are often related to time and space resolution, coverage, and cost. In the last two decades, several studies have been carried out to exploit opportunistic signals of terrestrial microwave communication links to improve precipitation estimation capability. This study describes and evaluates a method to retrieve rainfall rate from the signal-to-noise ratio obtained from commercial interactive digital video broadcasting (DVB) receivers [referred as to smart low-noise blocks (smartLNBs)]. During a 1-year measurement campaign carried out in Tuscany (Italy) with purposely deployed instruments, the precipitation values estimated from a set of SmartLNBs were compared with measurements from co-located rain gauges and disdrometer. The normalized mean absolute error (NMAE) and root-mean-square error (RMSE) obtained comparing the total cumulative precipitation from a SmartLNB and a disdrometer are 48.8% and 7.46 mm, respectively. Encouraging results also come from comparing the total precipitation amounts as measured by the SmartLNBs and by the rain gauges, with values of NMAE (respectively, RMSE) ranging between 44% and 82% (respectively, 5.2 and 11.5 mm).

Ahstract- In the NEFOCAST project we aim at estimating rainfall by the opportunistic use of the signal attenuation due to the propagation channel in satellite communications. The estimation is performed by reverse engineering the effects of the various propagation phenomena on the satellite signal. However, the accuracy of the estimation is affected by several factors: in first place the rapid fluctuations in signal amplitude caused by small-scale irregularities in the tropospheric refractive index; secondly, the perturbations of the orbit of GEO satellites, such as the gravitational effects of the moon and the sun, which, even if periodically counteracted by correction maneuvers, nevertheless cause residual orbit inclinations. The problem with all these factors is that they can cause large deviations in the clear-sky measurements that can be misinterpreted as rain events. In this paper we address these problems by employing two Kalman filters designed to track slow and fast changes of the received signal energy, so that the rain events can be reliably estimated.

NAdirosietalURSIATRASC2018summarypaperV4. The system does not require the set-up of specific precipitation measuring instruments, but uses telecommunication links. An algorithm that converts the SmartLNB raw data into attenuation values, and infers rainfall rate from the total signal attenuation provided by the devices and from the knowledge of the link geometry, has been developed. An experimental campaign will take place in 2018 in Tuscany with the purpose of validating the NEFOCAST estimates, obtained through a dense population of smartLNBs and an X-band dual-polarization weather radar, purposely installed. During a preliminary test phase, performance of the algorithm has been assessed tested by comparing data from individual smartLNBs with tipping bucket rain gauge and a co-located laser disdrometer. This study presents and discusses results obtained during the test phase, focusing on disdrometer evaluation.

In this work we propose a technique for 15-minutes cumulative rainfall mapping, applied over Tuscany, using Italian weather radar networks together with the regional rain gauge network. In order to assess the accuracy of the radar-based rainfall estimates, we have compared them with spatial coincident rain gauge measurements. Precipitation at ground is our target observable: rain gauge measurements of such parameter have a so small error that we consider it negligible (especially if compared from what retrievable from radars). In order to make comparable the observations given from these two types of sensors, we have collected cumulative rainfall over areas a few tens of kilometres wide. The method used to spatialise rain gauges data has been the Ordinary Block Kriging. In this case the comparison results have shown a good correlation between the cumulative rainfall obtained from the rain gauges and those obtained by the radar measurements. Such results are encouraging in the perspective of using the radar observations for near real time cumulative rainfall nowcasting purposes. In addition the joint use of satellite instruments as SEVIRI sensors on board of MSG-3 satellite can add relevant information on the nature, spatial distribution and temporal evolution of cloudiness over the area under study. For this issue we will analyse several MSG-3 channel images, which are related to cloud physical characteristics or ground features in case of clear sky.

The real-time measurement of rainfall is a primary information source for many purposes, such as weather forecasting, flood risk assessment, and landslide prediction and prevention. In this perspective, remote sensing techniques to monitor rainfall fields by means of radar measurements are very useful. In this work, a technique is proposed for the estimation of cumulative rainfall fields averaged over a large area, applied on the Tuscany region using the Italian weather radar network. In order to assess the accuracy of radar-based rainfall estimates, they are compared with coincident spatial rain gauge measurements. Observations are compared with average rainfall over areas as large as a few tens of kilometers. An ordinary block kriging method is applied for rain gauge data spatialization. The comparison between the two types of estimates is used for recalibrating the radar measurements. As a main result, this paper proposes a recalibrated relationship for retrieving precipitation from radar data. The accuracy of the estimate increases when considering larger areas: an area of 900 km2 has a standard deviation of less than few millimeters. This is of interest in particular for extending recalibrated radar relationships over areas where rain gauges are not available. Many applications could benefit from it, from nowcasting for civil protection activities, to hydrogeological risk mitigation or agriculture.