Intense precipitation phenomena occurring over the Tyrrhenian area between Tuscany, Corse Sardinia, and Liguria very
often cause floods with considerable socio-economic damages. The need of monitoring such events has led to the
implementation of an observing weather radar network: it firstly started with an S-band radar in Corse, three C-band
radars in Liguria, Tuscany and Sardinia. Recently, the implementation of an X–band network of three radars in Tuscany
and two further C-band radars in Sardinia completed the network. This work shows how this network can be used for the
characterization of weather events, following their development and dynamics and providing some information about
their possible evolution. Furthermore, the use of meteorological satellites observations can upscale the area of interest to
the mesoscale level and provide an enlarged temporal overview. For instance, the Meteosat Second Generation satellites
provide useful information about the air mass distribution, convective phenomena occurrence and microphysics in the
observed scene, by combining different spectral channels. Finally, ground based observations are meaningful for
assessing the observing capabilities of other instruments and for characterizing the effects on soil surface. For some
selected case studies, the different observing instruments were compared and a methodology to integrate them
synergically is presented and tested. Weather radars correctly detect the rainfall systems and their motion in all the case
studies. Clearly, the higher spatial resolution of X-band radars allows detecting the different precipitation areas with
great spatial details, while C- and S-band radars can detect phenomena at higher distances. Satellites images have lower
spatial resolutions but especially thanks to the RSS (Rapid Scan Service) they can help to detect the growing or
dissipating stage of the whole phenomena. Moreover the ground-based network confirms its relevance in improving the
identification of the precipitation intensity and in reducing the number of false alarms.
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.
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.