We present IT-DPC-SRI, the first publicly available long-term archive of Italian weather radar precipitation estimates, spanning 16 years (2010–2025). The dataset contains Surface Rainfall Intensity (SRI) observations from the Italian Civil Protection Department's national radar mosaic, harmonized into a coherent Analysis-Ready Cloud-Optimized (ARCO) Zarr datacube. The archive comprises over one million timesteps at temporal resolutions from 15 to 5 minutes, covering a 1200×1400 kilometer domain at 1 kilometer spatial resolution, compressed from 7TB to 51GB on disk. We address the historical fragmentation of Italian radar data - previously scattered across heterogeneous formats (OPERA BUFR, HDF5, GeoTIFF) with varying spatial domains and projections - by reprocessing the entire record into a unified store. The dataset is accessible as a static versioned snapshot on Zenodo, via cloud-native access on the ECMWF European Weather Cloud, and as a continuously updated live version on the ArcoDataHub platform. This release fills a significant gap in European radar data availability, as Italy does not participate in the EUMETNET OPERA pan-European radar composite. The dataset is released under a CC BY-SA 4.0 license.

Prediction of extreme precipitation with high spatial resolution on short time scales (i.e., nowcasting) is still challenging, and data-driven approaches such as artificial intelligence tools are increasingly being used. In this respect, two factors are undoubtedly important: 1) The need of robust databases of temporal and spatial evolution of rain precipitation patterns to train new nowcasting routines, and 2) the establishment of an exhaustive benchmark to evaluate the improvements brought by new prediction algorithms. This article aims to contribute to the two points just mentioned by describing a novel practical-to-use radar data screening method and by analyzing the performance of nine existing radar nowcasting techniques to establish a minimum acceptable performance (MAP) level. The radar dataset used consists of 111 955 frames (1.5 year of data) at 1 x 1 km(2) resolution sampled 5 min apart over Italy, a country scarcely investigated so far in terms of radar nowcasting compared to other areas, and that may be considered a climate change hotspot. The results reveal a large variability in the performance of each tested nowcasting method, an aspect often not considered in similar studies but that evidences as the differences among the skills of various nowcasting methods can be systematically masked by the natural variability of each prediction outcome. The seasonal dependence of the nowcasting capacity is also shown and linked to the presence of orographic convective precipitation that occurs in Italy during the summer season. This last point also suggests for the future the need to implement convection-specific nowcasting modules, possibly optimized on local areas to improve the forecasting skills in case of atmospheric instability.

In-cloud motion vector retrieval is of great interest in several atmospheric science research fields. Short time extrapolation of radar data (precipitation nowcasting), assimilation into numerical weather prediction models, study of atmospheric circulation, as well as reference scenarios for future satellite missions, are glaring example where the knowledge of in-cloud motion vectors can play a relevant role. In this work, a dataset of nearly one-year and half of measurements collected by ground-based weather radars over the Italian peninsula, is used to perform the reconstruction of horizontal in-cloud rain motion vectors (RMVs) using both optical flow-based solutions from literature and an innovative extension of the multiple Doppler solution that make use of mosaicked Doppler radar data. The outcomes of the techniques imple-mented are analyzed in terms of reference Doppler measurements, reanalysis wind fields from ERA5 and evaluating the impact of the RMVs in a semilagrangian precipitation nowcasting framework. To the author knowledge, this is the first attempt in quantitatively evaluating RMVs. Results show that the use of Doppler information enhances the dynamic range of the retrieved RMV intensity with respect to optical flow-based solutions giving a better agreement with the ERA5 too. In terms of precipitation nowcasting, the use of Doppler-driven RMV does not give significant improvements due to gradients shown by RMV intensity when constrained with the measured Doppler.

This work was born from a wish of remembering the fundamental contribution of Prof. Frank Silvio Marzano to the field of physical volcanology. In fact, for the last fifteen years and in the context of several European projects, Prof. Marzano collaborated with many volcanologists as well as scientists from different fields and wrote many scientific articles aimed at studying the dynamics of explosive eruptions. He left his imprinting in this research sector laying the foundations of radar volcanology in Italy, and extended his studies to other sensors. His work is relevant for the analysis of the main eruption source parameters needed to characterize the eruptive events. Here we show how remote sensing instruments applied to analyze explosive activity of different volcanoes worldwide, are going to increase the knowledge in this multidisciplinary research area and the awareness from the scientific community of the potential of these sensors at various wavelengths.

Several meteorological services across the world are using networks of weather surveillance radars with the aim of advancing timely monitoring and quantification of precipitation as well as increasing the forecast skills through data assimilation into numerical weather prediction (NWP) models. This contribution offers an overview of the most relevant applications of weather radars in the context of operational use of such systems. The applications covered span from algorithms to ensure sufficient radar data quality, to estimate near surface rain, to perform hydrometeor identification and recognize key in-cloud process of volcanic and atmospheric extreme events.

A severe weather event is analyzed in terms of cell development, dynamics, and thermodynamics. The Mediterranean region is sporadically affected by severe weather events causing damage and injuries. An unusual and severe weather event hit Italy on July 9-10, 2019 causing heavy damage. The meteorological structure was characterized by a trough entering the eastern Mediterranean area from northeastern Europe affecting Italy and the Balkans with cold-air advection over the Adriatic Sea. On the morning of 10 July 2019, a supercell developed along the coast north of Pescara (central Italy), producing intense rainfall (130 mm/3 h) and a heavy hailstorm with hailstones larger than 10 cm in diameter. In this work, the dynamics and thermodynamics for triggering and maintenance of the supercell are investigated using the numerical model WRF (Weather Research and Forecasting system), satellite, radar-data, soundings, and ground observations. In the companion paper, the role of SST and orography was investigated assessing that the SST spatial representation, its anomaly distribution and the topography play a key role in triggering the supercell (Ricchi et al., 2023). In this paper, high spatial and temporal-resolution model simulations and observations are used to investigate the characteristics of the supercell by analyzing both its horizontal and vertical structure. The hailstone characteristics are assessed by using the HAILCAST parameterization in which its performance and reliability are discussed.

Over the years, there has been an increase in extreme weather events which encouraged the scientific community to employ ever more different techniques for their studies. In this context, GNSS (Global Navigation Satellite System) find its place. Over the last thirty years, this technique has shown increasing applicability and reliability in the field of weather forecasting and analysis. However, there are points that it is critical to continue to investigate; one of the most discussed and noteworthy is the behavior of the GNSS-PWV (Precipitable Water Vapor from GNSS) time course during severe weather events. The relation between GNSS-PWV pattern and weather event evolution appears to be non-constant, sometimes showing a PWV peak at maximum convection, sometimes an advance and sometimes a delay. In this study we try to identify the causes of this unevenness of behavior using the number of lightning as a reference for the trend of convection and the VIL (Vertical Integrated Liquid content) obtained from radar as a term of comparison for the validation of GNSS-PWV.

On 10 July 2019, a giant hail-bearing supercell hit the Adriatic coast of central Italy. Hailstones with a maximum diameter of 14 cm were reported in the city of Pescara between 10:00 and 11:00 UTC. In this work, the main synoptic and mesoscale features, responsible for the triggering and the development of the supercell, are analyzed using the WRF model. The intrusion of Bora wind over the northern and central Adriatic was relevant for two reasons: on the one side, the arrival of low-level cold air produced an uplift of the pre-existing warm air and favored the triggering of convection; on the other side, the strong vertical wind shear, also due to the presence of intense upper-level southwesterlies, created conditions favorable to the formation of the supercell. The predictability of the event is also discussed, comparing simulations starting at different initial times and forced with GFS and IFS forecasts. The model results show that the runs initialized at earlier times reproduced more accurately the track and the time evolution of the supercell. The HAILCAST module of WRF was also used to simulate hailstorm characteristics, such as the average hailstone diameter. WRF-HAILCAST simulations proved to be in fair agreement with the radar reflectivity retrievals and with local reports.

On the morning of 10 July 2019, an intrusion of relatively cold and dry air, over the Adriatic Sea, through a "bora jet", gave rise to a frontal structure at the ground, which moved rapidly from the Northern to the Southern Adriatic. The intense thermal gradient (together with a high positive sea surface temperature anomaly), the interaction of the jet with the complex topography of Apennines and the coastal boundary, generated a storm structure that moved parallel to the central Italy coast. In particular, between 8UTC and 12UTC, a supercell developed along the coast to the north of Pescara city (middle Adriatic), producing rainfall that reached 130 mm in 3 hours, and a violent hailstorm (estimated diameter greater than 10 cm). In this work, the frontal dynamics and the genesis of the thunderstorm are studied using the numerical system COAWST. Local polarimetric radar observations are also used to check the consistency of the simulations in the mature phase of the supercell. Numerical experiments are performed using a 1 km grid over central Italy, initialized using the ECMWF IFS analysis/forecasts. The sensitivity study investigates the role of the orography, the sea surface temperature (SST) and the coupling between ocean and atmosphere. Orography tests include simulations where the relevant peaks of the Apennine range (such as Gran Sasso and Picentini) are removed as well as cases where their peaks are modified compared to their real values. In terms of SST, we employ, using an uncoupled approach, the ECMWF SST dataset, the MFS-CMEMS Copernicus dataset at 4 km, 0.01°C Satellite SST, and we investigate the role of the SST anomaly (adding +1°C and +2°C to the real field). The role of the ocean-atmosphere interaction is tested using the COAWST numerical model using an ocean model numerical grid at 1 km resolution over the whole Adriatic Sea. The preliminary results show that the topography and in particular the interaction with the peaks of the Apennine range plays a fundamental role in the dynamics of the cold pool that trigger the convective system. Also, the SST anomaly is found to play an important role in the development of the supercell. In particular, we observed that the simulations forced with MFS-CMEMS SST and the COAWST model runs produce a very realistic SST, in terms of spatial and temporal distribution, but colder by about 1.5 °C in absolute value if compared to observed satellite data. This difference generates lower heat fluxes, less evaporation, weaker precipitations and smaller hail than using warmer SSTs.

A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful for the implementation of early warning systems. The results highlight the validity of the different techniques and the consistency among the observations. This result, therefore, leads to the conclusion that a joint use of the innovative techniques with the operational ones can bring reliability in the description of events.

The National Aeronautics and Space Administration/Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) Core Observatory was launched on February 27, 2014. It carries, for the first time, a dual-frequency precipitation radar (DPR) designed to provide insights into the 3-D structure of precipitation by using its Ka- and Ku-band frequencies. In addition, the DPR is used as calibrator for the GPM microwave imager. Single-frequency (SF) (both Ku- and Ka-only) and dual-frequency (DF)-based products provide, among the others, the microphysical parameters (namely, the mass-weighted mean diameter Dm and the normalized intercept parameter Nw) of drop size distribution (DSD), as well as precipitation rates. This chapter focuses on reliability of the DSD parameters over the Mediterranean area by taking as reference the DSD parameters estimated by ground-based radar measurement. The results show a general overestimation of Dm and Nw as well as of rainfall rate. This is true during both stratiform and convective precipitations, while the performances are slightly better over sea than over land. Before of this, an intercomparison between the SF and DF DPR outputs is carried out to investigate the reliability of SF-based products when compared to the DF-based ones, treated as a reference. The results evidence how SF- and DF-based products provide similar reflectivity and rainfall rate values, but quite different estimates for Dm and Nw.

In recent years, the Mediterranean area has been affected by a continuous and significant increase in the intensity of violent weather events resulting in floods, hailstorms and tornadoes and an increasing impact on human activities, infrastructure and agricultural production. Among these extreme events, a particularly intense phenomenon occurred on July 10, 2019 affecting much of the central Adriatic coast. In particular, the Pescara area was affected by a supercell that produced heavy rainfall and an exceptional hailstorm, with hailstones even larger than 10 cm in diameter, causing extensive damage. This contribution documents, for the first time in Italy, the dynamics, morphology and main characteristics of the Pescara supercell [1] which was simultaneously observed, by two C-band meteorological radars of the national Department of Civil Protection (DPC). The results obtained highlight the irreplaceable role of dual-polarization Doppler weather radars in monitoring the evolution of hail, identifying the mesocyclone initiation and the related updraft and downdraft zones as well as their vertical extension, and highlighting the current limitations in determining the size of hail particles from radar measurements. Numerical simulations with the WRF model, using the HAILCAST module to simulate the evolution of hail, were carried out in order to evaluate the capabilities of an operational model in the simulation of such a particular event. In the context of the intensification of extreme events, this work is also a food for thought on the main aspects to be addressed in the near future to improve the chain of alerting and modelling of extreme events for prevention and civil protection. [1] M.Montopoli, E.Picciotti, L.Baldini, S.Di Fabio, F.S.Marzano, G.Vulpiani, "Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar", Atmospheric Research, Vol. 264, 15 Dec. 2021, 105852, https://www.sciencedirect.com/science/article/pii/S0169809521004087?dgcid=author

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events.

Ongoing climate change is considered to be responsible for the intensification and increased frequency of extreme weather events. The Mediterranean basin is not exempted by such modification processes as testified by the recent increase in the number of severe convective storm and mesocyclones. In recent years this kind of events caused many victims and damages in the such area, due to its geomorphological configuration and to the massive urban development of the last decades. Due to their local-scale nature Mediterranean extreme events can hardly be forecast with the use of numerical weather prediction systems and cannot adequately observed by using satellite platforms. These methods are not as straightforward as those based on radar observations because their spatial-temporal resolution is not necessarily adequate to resolve exceptional atmospheric phenomena as a weather radar can indeed offer. This article uses weather radar observations of an exceptional Mediterranean hail-bearing supercell that hit the central-eastern coast of the Adriatic Sea on 10 July 2019 causing flash flood and giant hail. Two operational dual-polarization Doppler C-band radars, managed by the Civil Protection Department of Italy, were able to observe, in an operational framework, the genesis and the evolution of the supercell, allowing for a detailed analysis of the event. Exploiting the unique time-resolved high-resolution three-dimensional available measurements, the combined use of a wind-field retrieval scheme and the interpretation of the dual-polarization radar observed features reveals some unique microphysical processes, rarely documented through radar observations at the Mediterranean latitudes. Results of this work improve the understanding of mechanisms and processes of giant hail formation and it is crucial to improve the nowcasting and forecasting skill for the early detection of hazardous meteorological events.

On 10 October 2018 an intense storm, characterized by heavy rainfall, hit the Sardinia island, reaching a peak of 452 mm of rain measured in 24 h. Among others, two particularly intense phases were registered between 3 and 6 UTC (Universal Coordinated Time), and between 18 and 24 UTC. The forecast of this case study is challenging because the precipitation was heavy and localized. In particular, the meteorological model used in this paper, provides a good prediction only for the second period over the eastern part of the Sardinia island. In this work, we study the impact of lightning data assimilation and horizontal grid resolution on the Very Short-term Forecast (VSF, 3 and 1 h) for this challenging case, using the RAMS@ISAC meteorological model. The comparison between the 3 h VSF control run and the simulations with lightning data assimilation shows the considerable improvement given by lightning data assimilation, especially for the precipitation that occurred in the eastern part of the island. Reducing the VSF range to 1 h, resulted in higher model performance with a good precipitation prediction over eastern and south-central Sardinia. In addition, the comparison between simulated and observed reflectivity shows an important improvement of simulations with lightning data assimilation compared to the control forecast. However, simulations assimilating lightning overestimated the precipitation in the last part of the day. The increasing of the horizontal resolution to 2 km grid spacing reduces the false alarms and improves the model performance.

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.

Recently, the interest on snowfall remote sensing and quantitative precipitation estimation is becoming a popular topic by both the scientific and operational communities. As a matter of fact, snow plays a key role in the hydrological cycle and Earth energy budget and clearly represents a meteorological hazard that can seriously compromise human activities and properties. In this study, we used a dual-polarization X-band weather radar to quantify the near-surface liquid equivalent snowfall rate, proposing a new parameterization based on the use of radar reflectivity factor and specific differential phase shift. This effort adds to several recent works, mainly focused on S-band weather radar systems, demonstrating that the use of the radar specific differential phase shift (Kdp) is able to enhance the estimation precision with respect to the more customary approaches making use of radar reflectivity factor alone. To demonstrate this concept also at X-band, some case studies were collected from December 2018 to May 2019 in the Southern Apennine Mountains in the area of Naples (Italy). They were used to compare the proposed radar based liquid equivalent snowfall rate estimations, based on Z and Kdp, with reference laser-optical disdrometer time series collected in the close reference site of Montevergine observatory. Findings show that also at X band the use of Kdp produces a better score between the radar-derived liquid equivalent snowfall rate and the reference one from the disdrometer.

The forecast of severe events at the local scale still remains challenging because of the multitude of physical processes involved on a wide range of scales. Improving the initial conditions (IC) of numerical weather prediction (NWP) models is a key point for good forecasting. Since limited-area models are nowadays operational at the kilometric scale (< 5 km), the assimilation of data from high-resolution space-time observations is crucial to correctly represent the state of the atmosphere at local scale.Radar and lightning data are both useful to improve the IC of NWP models for several reasons. Radar data is available with a high spatio-temporal resolution and provides information on hydrometeors and wind, while lightning data locates convection both spatially and temporally accurate.Recently, Federico et al. (2019) studied the impact of radar reflectivity factor and lightning data assimilation on the Very Short-Term Forecast (VSF) of the RAMS@ISAC NWP model for two intense precipitation events over Italy. They found that, despite an improvement of the rainfall VSF due to the assimilation of lightning and radar reflectivity factor data, the usefulness of the procedure is partially limited by the increase in false alarms, especially in case of high precipitation rates (> 50 mm/3h).In this work, we apply the methodology proposed by Federico et al. (2019) to an intense precipitation event occurred in Italy in November 2019. The RAMS@ISAC meteorological model is used here, with a horizontal resolution of 3km.RAMS@ISAC is initialized by a 3D-Var data assimilation scheme that uses both lightning and radar reflectivity factor data. Different 3D-Var data assimilation scheme settings are used to produce different ICs for the RAMS@ISAC model for the specific case. The sensitivity of the precipitation field prediction to changes in these ICs will be discussed.Keywords: lightning data assimilation, radar reflectivity factor data assimilation, very short-term forecast, numerical weather predictionReferenceFederico, S., Torcasio, R. C., Avolio, E., Caumont, O., Montopoli, M., Baldini, L., Vulpiani, G., and Dietrich, S.: The impact of lightning and radar reflectivity factor data assimilation on the very short-term rainfall forecasts of RAMS@ISAC: application to two case studies in Italy, Nat. Hazards Earth Syst. Sci., 19, 1839–1864, https://doi.org/10.5194/nhess-19-1839-2019, 2019.