This study presents a detailed analysis of the present and expected future extreme climate conditions over Italy through the use of some extreme indicators. Climate data for this analysis were provided by the regional climate model COSMO‐CLM, using different grid spacing to ascertain the real importance of using higher resolution climate data, especially over such a complex topography as Italy. Four simulations were carried out at spatial resolutions of 0.125° and 0.0715°, driven by ERA‐Interim Reanalysis and the CMCC‐CM global model. We investigated the ability of the model to represent realistically the climatology of a subset of climate indicators defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for precipitation and temperature. Several high‐resolution observational data sets available over some Italian regions were therefore used in order to offset the limited number of observations available over Italy in the E‐OBS data set and its coarse grid. We found that the increase in resolution could have interesting benefits in representing such extreme indices, especially in the more orographically complex areas. Finally, we investigated future climate changes regarding extreme weather events expected under anthropogenic climate change scenarios, employing the IPCC RCP4.5 and RCP8.5 greenhouse gas concentrations, showing that such events are expected to increase over Italy.

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).

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window.

In 2021, a group of seven Italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation, giving birth to the Italian Group of Disdrometry (in Italian named Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/, last access: 16 May 2023). GID has made freely available a database of 1 min records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network was composed of eight laser disdrometers belonging to six different Italian institutions (including research centres, universities, and environmental regional agencies). This work aims to document the technical aspects of the Italian DSD database consisting of 1 min sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same data record length, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens up new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records and beyond. The Version 01 GID database can be downloaded at https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022), while Version 02 can be downloaded at https://doi.org/10.5281/zenodo.7708563 (Adirosi et al., 2023). The difference among the two versions is the diameter–fall velocity relation used for the DSD computation.

In recent years, the scientific community has paid particular attention to the analysis of extreme events, such as heat waves, droughts, and intense rain events that have caused loss of human life and significant economic damage. Climate-related extremes generally produce large impact on infrastructures, especially on those with insufficient design, while some infrastructures may become inadequate under the effects of severe extremes. In the particular case of airports, the increase in frequency and severity of extreme weather events will worsen their deterioration rate. This work presents an analysis of the expected climate variability over Napoli Capodichino Airport, using climate projections generated by the Regional Climate Model COSMO-CLM. Simulations were performed over Italy, employing a spatial resolution of approximately 8 km. The time period simulated was 1979–2100, and, in particular, the CMIP5 historical experiment (based on historical greenhouse gas concentrations) was used for the period 1979–2005, while, for the period 2006–2100, two different simulations were performed, employing the Representative Concentration Pathways IPCC RCP4.5 and RCP8.5 greenhouse gas concentrations. The meteorological situations over the airport have been analyzed, along with the identification of conditions that could cause relevant impact on airport environment. In particular, extreme summer temperatures may exceed design standards, leading to heat damage to surfaces, while runways or aprons may have trouble due to the surface melting during peak heat periods. Long term changes in the directions of wind can adversely affect the usability of runways, while changes in wind shear could modify strength and frequency of clear-air turbulence. Analyses have been performed considering suitable Extreme Events Indicators (EWI), both on past trends and on numerical projections over future periods, with the aim of contributing to the definition of a risk assessment methodology based on the combination of the frequency and of the severity of meteorological hazards.