Radar-rainfall estimation is a complex process that involves several error sources, some of which are related to the environmental context. The presence of orographic obstacles heavily affects the quality of the retrieved radar products. In relatively flat terrain conditions, dual-polarization capability has been proven either to increase the data quality or to improve the rainfall estimate. The potential benefit of using polarimetric techniques for precipitation retrieval is evaluated here using data coming from two radar systems operating in Italy under complex-orography conditions. The analysis outlines encouraging results that might open new scenarios for operational applications. Indeed, the applied rainfall algorithm employing specific differential phase mostly outperformed the examined reflectivity-based retrieval techniques except for the analyzed winter storm. In the latter case, the likely contamination by frozen or melting snow tended to degrade the performance of the examined K-dp-based rainfall algorithms

We consider an exceptional storm -'Klaus' (January 2009) -its evolution on the Western Mediterranean Sea, and how the associated wind and wave conditions were modelled by seven of the major systems presently operational in this area. We intercompare the model results and then verify them and the related model ensemble versus the available measured data.Working with short-term forecasts (24 h) only, as expected, each model correctly anticipates the incoming of an exceptional storm. However, even at such limited range, we have found substantial differences among the results of the different models. The differences concern the time the storm should have entered the Western Mediterranean Sea, the peak values of wind speed and significant wave height, the general distribution of the fields, and the locations where the maxima were achieved.We have compared the model results versus the available measured data, wind from scatterometer, waves from altimeter, plus a few buoy data. We have found some inconsistencies in the results, model wind data being on average larger than the measured one, while the opposite was true for wave heights. However, the limited amount of data available and its different times and positions, at and off the centre of the storm, impede the drawing of any definite conclusion in this respect.On the whole we feel that our results, although related to a single storm, cast doubts on the reliability of a single forecast system to provide sufficiently reliable and accurate forecasts in case of an incoming exceptional storm. The results, both for wind and waves, have improved using an ensemble of the seven considered models. This suggests that there is no relevant systematic error in the used models except, as possibly suggested by our results, in the case of wave generation under very strong wind and very young sea conditions.

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.