Thunderstorms may cause large damages to infrastructures and population, therefore the possible identification of the areas with the highest occurrence of these events is especially relevant. Nevertheless, few extensive studies of these phenomena with high spatial and temporal resolution have been carried out in the Alps and none of them includes North-western Italy. To analyze thunderstorm events, the data of the meteorological radar network of the regional meteorological service of Piedmont region (ARPA Piemonte) have been used in this work. The database analyzed includes all thunderstorms occurred during the warm months (April to September) of a 6-year period (2005–2010). The tracks of each storm have been evaluated using a storm tracking algorithm. Several characteristics of the storms have been analyzed, such as the duration, the spatial and the temporaldistribution, the direction and the distance travelled. Obtained results revealed several important characteristics that may be useful for nowcasting purposes providing a first attempt of radar-based climatology in the considered region.

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (D_m), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (N_w).

Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z) to rainfall rate (R) relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses.

Rarely, a close correlation between wildfires and the occurrence of channelized debris flows has been observed in the Western Italian Alps. Only two cases in history have been reported, after brief and localized rainfall events of moderate intensity in Italy’s Piemonte region (NW Italy) caused debris flows, on 18 July 2005, in Verbania province (Pallanzeno municipality), and on June 2018 in Turin province (Bussoleno municipality). These phenomena occurred after a large portion of the catchments were affected by wide wildfires in the preceding months. Debris flow deposits showed an unusually large number of fine-grained particles, forming dark-brown mud-rich deposits associated with burnt wood deposits. Rainfall analysis related to the period between the wildfires’ occurrence and the debris flow events, using both raingauge and weather radar data, pointed out that the debris flows triggered in July 2005 and June 2018 were characterized by greater magnitude but associated with less precipitation intensity rates as compared with previous mud flows occurring just after wildfires. These behaviors can be explained by the presence of burned organic material and fine-grained sediment, generated from the soil’s thermal reworking, which formed a thick layer, centimeters deep, covering a large percentage of catchments and slopes. Most of this layer, generated by wildfires’ action were winnowed by rainfall events that had occurred in the months before the debris flow events, of significant magnitude, exhuming a discontinuous hydrophobic soil surface that changed the slopes’ permeability characteristics. In such conditions, runoff increased, corrivation time shortened, and, consequently, discharge along the two catchments’ channels-network increased as well. Consequently, the rainfall effects associated with rainfall events in July 2005 and June 2019 were more effective in mobilizing coarse sediments in channel beds than was typical for those catchments.

Radio Frequency Interference (RFI) is one of the main issues in weather radar community. Data quality and post-processing algorithm, such as quantitative precipitation estimation and hydrometeor classification, are often affected by interferences. C-band radars share their operational frequency band with Radio Local Area Network (RLAN) and Wireless Area Network (WLAN), which may cause harmful interferences in radar systems. Nowadays, in northwestern Italy, the X-band weather radar managed by Arpa Piemonte is also receiving interfering signals. This work aims to introduce the RFIs phenomena affecting both C-band and X-band weather radars in Piemonte region, Italy. A preliminary method to detect the interfering sources at C-band is discussed, cross-checking data available in the regional database of electromagnetic sources and in-field measurements. A six-day measurement campaign was performed using the X-band radar as receiving antenna to collect an extensive dataset of interfering signals. The polarimetric features of the acquired RFI dataset are presented. The X-band RFIs show a day–night pattern, likely caused by human-related activities. The growth of wireless telecommunication systems, such as HiperLAN in northwestern Italy, and the continuous demand of electromagnetic spectrum portions make the understanding of electromagnetic interferences in weather radars the primary concern to ensure the data quality.