During the last 15 years, weather extremes caused several disruptions to the Italian electric system. Their increasingly occurrence is mainly due to the exchanges along the meridians of air masses with very different thermal, density and moisture content properties. The Italian transmission system operator and the distribution companies have repeatedly stressed the need to have a reliable and updatable weather dataset with a history of at least 15 years to improve the resilience of the electric system. The aim of this work is to develop a new MEteorological Reanalysis Italian DAtaset (MERIDA) able to respond to the energy stakeholders, who need reliable meteorological data to implement effective adaptation strategies to operate the electric system safely. MERIDA consists of a dynamical downscaling of the new European Centre for Medium‐range Weather Forecasts (ECMWF) global reanalysis ERA5 using the Weather Research and Forecasting (WRF) model, which is configured to describe the typical weather conditions of Italy. Furthermore, the optimal interpolation (OI) technique is applied to the modelled 2 m temperature and precipitation data through the use of meteorological observations of the Regional Agencies for Environmental Protection. MERIDA is verified against COSMO REA6 of the Deutscher Wetterdienst (DWD) and ERA5 itself for the period 2010–2015, showing comparable or better results in the reconstruction of 2 m temperature and precipitation. The best results are obtained with MERIDA post‐processed by the OI. Some severe weather events that determined important electric disruptions are also analysed, showing that MERIDA is able to identify the meteorological conditions leading to significant events of wet snow, heatwaves and floods through their correct spatial and temporal location and through a quantitative assessment of each atmospheric phenomenon.

Abstract. Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly.The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

Recent observation and modeling-based studies have shown how air quality has been positively affected by the containment measures enforced due to the COVID-19 outbreak. This work aims to analyze Lombardy’s NO2 atmospheric concentration during the spring lockdown. The region of Lombardy is known for having the largest number of residents in Italy and high levels of pollution. It is also the region where the first European confinement measures were imposed by the Italian government. The modeling suite composed of CAMx (Comprehensive Air Quality Model with Extensions) and WRF (Weather Research and Forecasting model) provides the setting to compare the atmospheric NO2 concentration from mid-February to the end of March with a business as usual situation. The main interest in this work is to investigate the response of NO2 atmospheric concentration to increasingly reduced road traffic. We can simulate, for the first time, a real circumstance of progressively reduced mobility, as well as validating it with measured air quality data. Focusing on the city of Milan, we found that the decrease in NO2 concentration reflects progressively reduced traffic contraction. In the case of a large traffic abatement (71%), the concentration level is reduced by one third. We also find that industrial activities have a relevant impact on NO2 atmospheric concentration, especially in the provinces of Brescia and Bergamo. This study provides an overview of how incisive policies must be implemented to achieve the set environmental targets and protect human health.

A thorough investigation of power system security requires the analysis of the vulnerabilities to natural and man-related threats which potentially trigger multiple contingencies. In particular, extreme weather events are becoming more and more frequent due to climate changes and often cause large load disruptions on the system, thus the support for security enhancement gets tricky. Exploiting data coming from forecasting systems in a security assessment environment can help assess the risk of operating power systems subject to the disturbances provoked by the weather event itself. In this context, the paper proposes a security assessment methodology, based on an updated definition of risk suitable for power system risk evaluations. Big data analytics can be useful to get an accurate model for weather-related threats. The relevant software (SW) platform integrates the security assessment methodology with prediction systems which provide short term forecasts of the threats affecting the system. The application results on a real wet snow threat scenario in the Italian High Voltage grid demonstrate the effectiveness of the proposed approach with respect to conventional security approaches, by complementing the conventional "N − 1" security criterion and exploiting big data to link the security assessment phase to the analysis of incumbent threats.

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