A workshop on high-impact meteorological events in Italy was jointly organized by the newly formed Italia Meteo Agency (AIM) and the Italian Association of Atmospheric Sciences and Meteorology (AISAM) on September 27, 2022. The aim of the workshop was to promote the sharing of the experiences that all operational and research meteorological centers in Italy have gained on specific types of high-impact events, favoring the establishment of a common know-how of Italian operational meteorology.

Italy and Germany are establishing a new bilateral cooperation in meteorology, climatology and related disciplines that will create a hub of excellence for cutting edge research and translate the findings into improving operational services and university education. The joint research and education network IDEA-S4S will harness expertise of universities, research institutes and operational services in both countries and foster scientific exchange and collaboration to improve weather, climate and environmental services. Within the new programme, the core areas of scientific expertise of both countries will be systematically brought together, activities will be streamlined and extended beyond basic research collaboration to form a holistic IDEA-S4S network of weather and climate science and education, extending from qualification of graduates to support for early career researchers to networking of senior scientists. The programme will cooperate with WMO and the European Meteorological Infrastructure (ECMWF, EUMETSAT, EUMETNET) and strengthen the scientific environment for the ECMWF sites in Bonn and Bologna. A Joint Steering Committee will oversee the cooperation and guide the overall scientific and strategic direction of the programme. Through four-year funding periods, the network aims to make substantial progress in seamless high-resolution probabilistic Earth system prediction, employing state-of-the-art observing systems and Earth system models. This requires improved understanding and application of coupled processes between the components of the Earth system (including atmosphere-ocean-ice-land-vegetation-rivers) as well as between the impacts of human activities and the Earth system. Such complex weather and climate prediction systems place high demands on high-performance computing infrastructure, generate extremely large data volumes and need to integrate observations seamlessly into the models. In this contribution, we will present the concept and roadmap of the IDEA-S4S network, which will focus on improving seamless weather and climate forecasts, in particular for high impact events such as floods and droughts. Both countries have experienced devastating impacts of such extreme events with unusual rainfall intensities, some of which even lead to the destruction of entire regions. Better understanding the complex structure and the numerous feedback processes in such events will improve the prediction of future events in support of a better prepared and resilient society.

At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps.

The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by several authors throughout the world, with different developments observed depending on the continent. Following a period of approximately 40 days of almost continuous rain that occurred from October to November 2019 across the Italian territory and caused several hazards (e.g., floods and landslides), a relevant question for decision-makers and civil protection actors emerged regarding the relative frequencies of given rainfall events in the Warning Hazard Zones (WHZs) of Italy. The derived products of this work could answer this question for both weather and hydrogeological operators thanks to the frequency and spatio-temporal distribution analyses conducted on 10-year daily rainfall data over the entire Italian territory. This work aspires to be an additional tool used to analyse events that have occurred, providing further information for a better understanding of the probability of occurrence and distribution of future events.

Observational daily precipitation data from a group of 1762 stations over north-central Italy and adjacent areas are used to produce a high resolution daily gridded precipitation analysis covering the period from 1961 to 2015. Input data are checked for quality, time consistency, synchronicity and statistical homogeneity and the final result has been used to describe the spatial and temporal variability of precipitation over the area. Data are interpolated using a modified Shepard scheme and the interpolation errors are compatible with those presented in Isotta et al. (Int J Climatol 34(5):1657–1675, 2014). The analysis is compared with other similar products available over the area considered, and differences and similarities are described, taking into account the impacts of different spatial resolution and time coverage. The data set is used to describe local climate with respect to precipitation, including mean values and seasonality, by using a group of climate annual and seasonal indices: cumulated precipitation, maximum number of consecutive dry days, frequency of wet days, mean precipitation intensity and 50th and 90th percentile of daily precipitation over a season. The linear trends over the full period of these indices are described and compared. It is shown that although the time series of area average total annual precipitation over north-central Italy does not show significant linear trends, these are present locally. In particular, significant negative trends of annual total precipitation are found in central Italy and in the inner part of northern plains, while significant positive linear trends are present in several areas over the Alps and over the Liguria coast. The seasons most affected by changes in precipitation are summer and autumn, which, in most areas, are the driest and wettest seasons. In summer, significant positive trends in total precipitation have been found in areas close to the northern national borders, while significant negative trends are located elsewhere. The number of wet days is significantly decreasing over most of the domain, but the 90th percentile of precipitation is significantly increasing over most of the Alpine area and northern Po Valley. Over the southern part of the Po Valley and central Italy summer precipitation is significantly becoming less frequent and, generally, less intense. In autumn, total precipitation is characterised by significant positive trends over large areas in Northern Italy and by significant negative trends in inner areas of the Central Apennines. The trend patterns present great similarities with those of the 90th percentile of daily precipitation for the same season. The maximum length of dry spell is significantly decreasing in autumn over most areas, including central Italy, while the number of wet days presents negative but mostly non significant trends over the whole domain.

This work analyzes the frequency of days characterized by daytime temperature inversion and air stagnation events in the Po valley area. The analysis is focused on both historical series and future projections under climate change. Historical sounding data from two different Italian stations are used as well as future projections data, provided by CMCC-CCLM 4-8-19 regional climate model (MED-CORDEX initiative). A new method to detect layers of temperature inversion is also presented. The developed method computes the occurrence of a temperature inversion layer for a given day at 12 UTC without a detailed knowledge of temperature vertical profile. This method was validated using sounding data and applied to the model projections, under two different emissions scenarios (RCP4.5 and RCP8.5). Under RCP4.5 intermediate emissions scenario, the occurrence of temperature inversions is projected to increase by 12days/year (around+10%) in the last decade of 21st century compared to 1986–2005 average. However, the increase in temperature inversions seems to be especially concentrated in the warm period. Under RCP8.5 extreme scenario, temperature inversions are still projected to increase, though to a lesser extent compared to RCP4.5 scenario (+6days/year in the last decade of 21st century). A similar trend was found also for air stagnation events, which take into account the variation of precipitation pattern and wind strength. The expected increases are equal to +13days/year and +11days/year in the last decade of 21st century compared to 1986–2005 average, under RCP4.5 and RCP8.5 scenarios respectively.

The impacts of climate change on Po River discharges are investigated through a set of climate, hydrological, water-balance simulations continuous in space and time. Precipitation and 2 m mean temperature fields from climate projections under two different representative concentration pathways, RCP4.5 and RCP8.5, have been used to drive the hydrological model. Climate projections are obtained nesting the regional climate model COSMO-CLM into the global climate model CMCC-CM. The bias in climate projections is corrected applying the distribution derived quantile mapping. The persistence of climate signal in precipitation and temperature after the bias correction is assessed in terms of climate anomaly for 2041–2070 and 2071–2100 periods versus 1982–2011. To account for the overall uncertainty of emission scenarios, climate models and bias correction, the hydrological/water balance simulations are carried out using both raw and bias corrected climate datasets. Results show that under both RCPs, either considering raw and bias corrected climate datasets, temperature is expected to increase on the whole Po River basin and in all the seasons; the most significant changes in precipitation and discharges occur in summer, when the reduction of precipitation leads to an increase in low flow duration and occurrence likelihood, and in autumn and winter where precipitation shows a positive variation increasing the high flows frequency.

ABSTRACTIn the region of the European Alps, national and regional meteorological services operate rain‐gauge networks, which together, constitute one of the densest in situ observation systems in a large‐scale high‐mountain region. Data from these networks are consistently analyzed, in this study, to develop a pan‐Alpine grid dataset and to describe the region's mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analyses are based on a collation of high‐resolution rain‐gauge data from seven Alpine countries, with 5500 measurements per day on average, spanning the period 1971–2008. The dataset is an update of an earlier version with improved data density and more thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with a distance‐angular weighting scheme that integrates climatological precipitation–topography relationships. Scales effectively resolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. We quantify the uncertainty of the dataset by cross‐validation and in relation to topographic complexity, data density and season. Results indicate that grid point estimates are systematically underestimated (overestimated) at large (small) precipitation intensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations in indicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complex role of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M project and is freely available for scientific use.

Monitoring and description of the observed climate are extremely important not only on the scientific point of view, but also for their contribution to decision making at political level. In fact, they represent both a crucial contribution to the assessment of the climatological characteristics of a region, and a starting point for all activities related with planning of infrastructures, land use and agricultural and energy production. In Italy, climate monitoring activities are currently covered at local level by Regional Meteorological Services, and at national level by National Institutions like Aereonautica Militare and CRA-CMA. The project ARCIS (ARchivio Climatologico per l'Italia Settentrionale), started in 2008 and now approaching its first renewal, represents an effort done by some Italian Regional Meteorological Services in order to coordinate their activities in the field of climatology. The main target of this project consists of building a unified, high-density and long term climatological data base, which can be used in order to issue climatological products at super-regional scale, using shared methodologies for the treatment of climatological data. In the present note is a description of the project, of its aims, of some preliminary results and of its future perspectives.

Future changes of seasonal minimum and maximum temperature over Northern Italy are assessed for the periods 2021–2050 and 2070–2099 against 1961–1990. A statistical downscaling technique, applied to the ENSEMBLES-Stream1 and CIRCE global simulations (A1B scenario), is used to reach this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The set-up of the statistical scheme is done using large-scale fields (predictors) derived from ERA40 reanalysis and seasonal mean minimum and maximum temperature (predictands) derived from observational data at around 75 stations, distributed over Northern Italy, over the period 1960–2002. A similar technique is also applied to the number of frost days and ice days at a reduced number of stations in order to construct projections on change of the selected extreme temperature indices for the two future periods. The evaluation of future projections for these extreme indices is relevant due to its impacts on transports, health, and agriculture. The downscaling scheme constructed using observed data is then applied to large-scale fields simulated by global models (A1B scenario), in order to construct scenarios on future change of seasonal temperature, mean and extreme indices, at local scale. The significance of changes is tested from the statistical point of view. The results show that significant increases could be expected to occur under scenario conditions in both minimum and maximum temperature, associated with a decrease in the number of frost and ice days in both periods and more intense to the end of the century.

Observed precipitation fields show a high variability both in space and time and the amount of rainfall could vary a lot within a short distance.(Zepeda-Arce et al.,2000). The increasing of horizontal resolution in NWP models seems to enable them to reproduce this variability, even if frequent errors in time and space positioning make difficult a grid-point based employment of models QPF. In order to asses the ability of the models in reproducing the variability of the precipitation fields, we investigated the statistical properties of the observed and forecasted rain values falling within a predefined geographical area and in a specific time period (also called boxes). In particular we studied the distribution function (pdf )and evaluated some summarizing quantities, such as the mean, the maximum value and quantiles for each of the selected box. Results for different size of the chosen areas and period of time are used to validate the QPF of the COSMO suites that run operationally at ARPA-SIMC (COSMO-I7 and COSMO-I2) in comparison with the global model IFS-ECMWF.