Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The present paper evaluates radar error as a function of the range, in order to correct the rainfall radar estimates. The radar is calibrated utilizing data from the rain gauges. Then, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount is calculated against the slant range. The trend of the G/R ratio shows two behaviours: a concave part due to the melting layer effect close to the radar location and an almost linear, increasing trend at greater distances. A best fitting line is used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology is verified by comparing pairs of rainfall time series that are observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor is investigated at the scale of event, both for convective and stratiform events. The main result is that there is not a univocal range error pattern, as it also depends on the characteristics of the considered event. On the other hand, the adjustment factor tends to stabilize itself for time aggregations of the order of one year or greater.

Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.

Flash flood events are floods characterised by a very rapid response of basins to storms, often resulting in loss of life and property damage. Due to the specific space-time scale of this type of flood, the lead time available for triggering civil protection measures is typically short. Rainfall threshold values specify the amount of precipitation for a given duration that generates a critical discharge in a given river cross section. If the threshold values are exceeded, it can produce a critical situation in river sites exposed to alluvial risk. It is therefore possible to directly compare the observed or forecasted precipitation with critical reference values, without running online real-time forecasting systems. The focus of this study is the Mignone River basin, located in Central Italy. The critical rainfall threshold values are evaluated by minimising a utility function based on the informative entropy concept and by using a simulation approach based on radar data. The study concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed alarms.

In order to estimate the rainfall fields over an entire basin raingauge, pointwise measurements need to be interpolated and the small-scale variability of rainfall fields can lead to biases in the rain rate estimation over an entire basin, above all for small or medium size mountainous and urban catchments. For these reasons, several raingauges should be installed in different places in order to determine the spatial rainfall distribution during the evolution of the natural phenomena over the selected area. In technical applications, many empirical relations are used in order to deduce heavy areal rainfall, when just one raingauge is available. In this work, we studied the areal reduction factor (ARF) using radar reflectivity maps collected with the Polar 55C, a C-band Doppler dual polarized coherent weather radar with polarization agility and with a 0.9° beamwidth. The radar rainfall estimates, for an area of 1 km², were integrated for heavy rainfall with an upscaling process, until we had rainfall estimate for an area of 900 km². The results obtained for a significant amount of data by using this technique are compared with the most important relations of the areal reduction factor reported in the literature.