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The Mediterranean area is often struck by severe weather events and deep convective events because of the presence of the warm sea, the complex orography of the area, and the specific synoptic scale environment. This scenario is worsened by climate change because, as climate change is affecting many weather and climate extremes, and the frequency and intensity of heavy precipitation events have increased in most of the world.Over the past years, the use of Numerical Weather Prediction (NWP) models, along with an increasing availability of computing power, led to an improvement of the forecast accuracy. However, NWPs have well-known difficulties in capturing the physical processes at small spatial and temporal scales which are involved in convective or severe weather events. In this work we study the impact of assimilating GPS-ZTD (Global Positioning System-Zenith Total Delay) on the precipitation forecast over Italy for the month of October 2019, characterized by several moderate to intense precipitation events. The Weather Research and Forecasting (WRF, version 4.1.3) is used with its 3DVar data assimilation system. The horizontal resolution is 3km while the vertical domain spans the whole troposphere and lower stratosphere.A dense network of about 500 GPS receivers was used for data assimilation and verification of the atmospheric water content. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area.Results show that WRF underestimates the atmospheric water content for the period, and GPS-ZTD data assimilation reduced this underestimation by increasing the water content of the atmosphere. The GPS-ZTD data assimilation increases the precipitation forecast amount, and the model performance are improved up to 6h.Results for a case study show that the GPS-ZTD data assimilation can improve the precipitation forecast in different ways: predicting rainfall missed by the model without data assimilation or better focusing the precipitation already predicted by the model without GPS-ZTD data assimilation on the impacted area, the main drawback being the prediction of false alarms.

The impact of the aerosol optical properties on the ultraviolet index (UVI) in the urban area of Rome is investigated in this study. In particular, the influence of aerosol optical depth (AOD) and single scattering albedo (SSA), estimated at the wavelength of 340 nm, and of the Ångström exponent, calculated in the range 340–500 nm, over a period of 11 years (2010–2020) in the months from March to September are analyzed. The UVI is monitored by a Brewer spectrophotometer, whereas measurements of the direct Sun and diffuse sky irradiances are performed by a co-located PREDE-POM sun–sky radiometer of the ESR/SKYNET network; the aerosol optical properties are obtained by the Skyrad MRIv2 retrieval. A novel method, based on physical principles and easily adaptable to other contexts, is developed to extrapolate the aerosol properties to the UV range during periods when only visible to near-infrared measurements are available. The retrievals from the sun–sky radiometer are consistent with the chemical characterization of urban PM10 (particulate matter 10 mm or less in diameter) samples collected during an intensive field campaign held in summer 2011 at the same site (URBan Sustainability Related to Observed and Monitored Aerosol – URBS ROMA). The PM macro components identified during the campaign are grouped in order to evaluate the contribution of the main macro sources (soil, sea, secondary inorganic, organics, and traffic) whose relative role is indeed expected to strongly affect the aerosol absorption capability. The surface forcing efficiency, calculated as the change in the UV index for a unit AOD variation, shows that AOD is the primary parameter affecting the surface irradiance under clear-sky conditions in Rome. SSA and the Ångström exponent are also identified as secondary influencing factors, i.e., the surface forcing efficiency is found to be greater for smaller zenith angles and for larger and more absorbent particles in the UV range (such as, e.g., mineral dust).

The impact of assimilating GNSS-ZTD (Global Navigation Satellite Systems - Zenith Total Delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, characterized by several moderate to intense precipitation events. The WRF (Weather Research and Forecasting) model 4.1.3 is used with its 3DVar data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The water vapor forecast is verified for the forecast hours 1–6 h after the last data assimilation time, while the precipitation forecast is verified in the phases 0–3 h and 3–6 h after the last data assimilation time. More than 3000 rain gauges spread over Italy were used to verify the precipitation forecast. The application of GNSS-ZTD data assimilation to a case study showed an improvement of the precipitation forecast in different ways, the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on the precipitable water vapor and rainfall forecast, with an improvement of the performance up to 6 hours, and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm/3 h).

The purpose of the data assimilation is to use all the available information to determine as accurately as possible the state of the atmosphere. In this context we consider the assimilation of the GPS-ZTD (Global Positioning System – Zenith Total Delay) by a nudging scheme in Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate (RAMS@ISAC) at medium horizontal resolution (5 km). The water vapour mixing ratio ( q v ) given by the RAMS@ISAC is perturbed according to the differences between observed and simulated ZTD. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over central Italy area, over a one-month time frame. The studied network consists of 41 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performance. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using goGPS, an Open Source Program Package for GNSS Positioning. For the application of PPP processing to single-frequency receivers, goSEID method (an alternative implementation of the SEID approach integrated in the goGPS open source software) was applied; the algorithm, starting from the observations of a unique (or more) dual frequency “reference” receiver, is able to generate L2 synthetic observations for any single frequency receiver located in its surroundings (15–20 km). Results show that the assimilation of GPS-ZTD has a positive impact on the simulation of the Integrated Water Vapour over the areas where GPS receivers are located. The results obtained, using data assimilation, were indeed compared with those obtained by the model without data assimilation of GPS-ZTD. The two outputs were subsequently evaluated against observations obtained from two geodetic receivers (ROUN and INGR) not used in the assimilation process. The result showed that the independent observation was more consistent with the model output in which also the single-frequency receivers had been assimilated. The assimilation of GPS-ZTD improves also the simulated precipitation, especially by reducing false alarms.

Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may show some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex centre and a warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone (TLC) characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure) associated to shallow or deep warm core. In this study a comparative analysis among the Medicanes occurred during the Global Precipitation Measurement (GPM) era (i.e. since March 2014), is carried out. The goal is to extract common features from passive MW measurements to identify and characterize the transition to TLC phase during the Medicane evolution. Passive microwave measurements from the GPM constellation radiometers are used to characterize the precipitation structure and warm core properties throughout the Medicane evolution. In particular, the NASA/JAXA GPM Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used in conjunction with ground-based LIghtning NETwork (LINET) measurements to analyse the rainband structure and infer microphysics processes and convection strength. On the other hand, MW temperature sounding channels available from AMSU-A and ATMS radiometers are used to identify the warm core and infer its properties (e.g., depth and symmetry) around the cyclone center. The most intense Medicane on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020, is anlaysed in detail. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane TLC phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Moreover, the GPM-CO overpasses highlight a significant change in deep convection features between Ianos development and mature phases, which explain the substantial drop in lightning activity during Ianos TLC phase. The study demonstrates the value of satellite MW measurements in the GPM era to provide evidence of Medicanes' transition to TLC phase and to characterize its precipitation structure and microphysics processes.

In response to damage resulting from the natural phenomena of aging and decay but also from the occurrence of disasters (earthquakes, floods, fires, etc.), the conservation strategies of cultural heritage (whether it is movable or immovable) inevitably require a methodological approach aimed at planned conservation and preparedness for the risk event of the cultural site. Between 2007 and 2012, in this context, the intervention of recovery and conservation of the archaeological site of Villa Romana del Casale in Piazza Armerina (Sicily, Italy), UNESCO World Heritage Site (since 1997), was realized. The project, directed by the Centro Regionale per la Progettazione e il Restauro, Regione Siciliana, was aimed not only at performing the conservative intervention of the monumental site and its decorative apparatus, but also at drawing up a protocol for preventive maintenance. This protocol concerned the new covering system of the archaeological site, its protection and fruition, as well as the hydrogeological asset of the territory on which it rises. In this frame, since the early Middle Ages the fonts document serious floods that interested the area on which the Villa del Casale is located. These floods inevitably interacted with its conservation history, even influencing its existence. The damages caused by the flood of October 1991 are documented, when the archaeological area was already excavated thanks to the archaeological campaign directed by the archaeologist Gino Vinicio Gentili. These excavations were themselves influenced by the flood of 1951 that covered the area still partially explored. In this study, after considering the damage caused by the previous weather event of October 1991 on the UNESCO site, the occurrence of October 2021 and the construction’s positive response to the stress caused by the meteorological phenomenon are analysed. The investigation was carried out using ground-based and satellite-based measurements, to provide a detailed overview of the extreme event that occurred, to identify the pressures that insisted on the studied site. The analysis highlights the effectiveness of recovery and conservation project carried out on the monumental complex and completed in 2012. Keywords: UNESCO site, Archaeological area, Meteorology, Extreme weathers, Flooding, Safeguarding References - Meli, G. (Ed.) (2007). Progetto di recupero e conservazione della Villa Romana del Casale di Piazza Armerina, Palermo, Collana I Quaderni di Palazzo Montalbo. I Grandi Restauri, 12/1, C.R.P.R., Palermo.- Coletta, V., Mascitelli, A., Bonazza, A., Ciarravano, A., Federico, S., Prestileo, F., Torcasio, R.C. & Dietrich, S. (2021). Multi-instrumental Analysis of the Extreme Meteorological Event Occurred in Matera (Italy) on November 2019. In International Conference on Computational Science and Its Applications (pp.140-154). Springer, Cham.- Prestileo, F., Mascitelli, A., Meli, G., Petracca, M., Giorgi, C., Melfi, D., Puca, S. & Dietrich, S. (2022). Resilience of cultural heritage in extreme weather conditions: the case of the UNESCO Villa Romana del Casale archaeological site’s response to the Apollo Medicane in October 2021. In International Conference on Computational Science and Its Applications (In Press). Springer, Cham.

Typical features of lightning distribution in the mountain area of Mt. Cimone (2165 m a.s.l. - Northern-Central Italy) have been studied through detections provided by the ground-based LIghtning NETwork data (LINET) and the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS-LIS). The study was performed within the context of lightning implications as natural hazard, and its role in a changing climate. Of particular interest are mountain regions because of their orographic impact, which determine most lightning hotspots around the globe. LINET VLF/LF radio measurements allowed the characterization of both cloud-to-ground (CG) and intra-cloud (IC) strokes' geographical distribution and altitude of occurrence over 2012 through 2020. The lightning distribution showed a remarkable clustering of CGs at the mountain top in contrast to a homogeneous distribution of ICs, highlighting the likely impact of orography. IC strokes peaked around 4 to 6 km altitude, consistency with the observed typical cloud range. The joint exploitation of LIS-ISS optical observations of LINET detections extended the study to further features of flashes not seen in radio wavelengths and stands as cross-validation of the two detection methods over such a complex orography. These results give an example of mountain-driven changes in lightning occurrence. The clustering at the Cimone mountain top induced by the orography replicates a general feature of the dependence of global lightning hot-spots from elevation and is of great interest in the understanding of the lighting-climate relationship, considering known effects of elevation-depedent climate change.

The Mediterranean Basin is often hit by severe meteorological events, that can cause floods and flash floods. The intensity of these storms is both due to the presence of a warm sea, that contributes to feed the storm with high water vapor amounts, and to the complex orography of the region, which intensifies the precipitation over specific areas. The prediction of these events is very challenging, since different spatial and temporal scales are involved. Numerical Weather Prediction (NWP) models with a high spatial horizontal resolution are able to represent these kinds of events, but without a high precision in space, time and amount. For a better representation of extreme rainfall events, an important role can be played by the information given at the local scale to the NWP models by initial conditions. Data Assimilation (DA) can be a fundamental instrument to help NWP models to improve their prediction, through the production of better initial conditions. However, DA needs observational data, and there is a lack of meteorological data in open sea, where radar data are not available. In this context, satellite observational data are very interesting because they can provide data both over sea and over land. The AEROMET (AEROspatial data assimilation for METeorological weather prediction) project aims to study the satellite rain-rate assimilation in the Weather Research and Forecasting (WRF) model to improve the prediction of convective meteorological systems, with a particular focus to systems which originate over the sea. The assimilation method considers a certain rain-rate threshold, which is representative of convective precipitation, avoiding in this way to add an excessive water vapor amount to the model. In this work, we show the preliminary results of the AEROMET project. Examples are presented to show the feasibility of the method and statistics will be shown to quantify its impact on rainfall prediction. ACKNOWLEDGMENTS This work was done in the framework of the AEROMET project (A0375-2020-36588 - “Progetti di Gruppi di Ricerca 2020” LazioInnova - FESR Fondo Europeo di Sviluppo Regionale Programma Operativo regionale del Lazio).

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.

Lightning is an important threat to life and properties and its forecast is important for practical applications. We show the performance of a dynamic lightning scheme for the next-day strokes forecast. The prediction is compared against the LINET network, and the forecast period spans one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021. The events span a wide range of lightning intensity; 69 cases occurred in summer, 46 in fall, 18 in winter, 29 in spring. Three different settings of the lightning scheme are considered to test the sensitivity of the method to the key parameter of charge transferred in 1 second: 0.5*10-4 C (L50), 0.75*10-4 C (L75), and 1.0*10-4 C (L100). The meteorological driver is WRF. Each simulation lasts 36h and the first twelve hours are the spin-up time and are discarded from the analysis. The focus is on the next-day forecast (12-36 h). The horizontal resolution of the simulations is 3 km and 50 unevenly spaced vertical levels extend from the surface to 50 hPa. Lightning is closely related to convection in the atmosphere and model errors in the lightning forecast have two main sources: errors in forecasting the convection and errors in the representation of the electric processes inside the clouds. This makes the lightning forecast a difficult task. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and sea. LINET strokes are remapped into the WRF 3km grid and then further elaborated for comparison with the strokes forecast. Among the three configurations of the lightning scheme, L75 forecasts accurately the total number of strokes recorded for all the cases, L50 underestimates the strokes and L100 overestimates the strokes. The time-series correlation of daily observed and forecasted strokes is around 0.75 and depends on the season. Qualitative scores (FBIAS, ETS, POD, FAR) computed for the 3km grid and different strokes thresholds have low values and upscaling the model output, by summing the forecast and observed strokes over grids with larger grid spaces (from 6 to 48 km), improves the results. Among the different configurations of the dynamic lightning scheme, L75 performs slightly better. However, L50, L75, and L100 show very similar spatial patterns of predicted strokes. The analysis of the fraction skill score shows that the best lightning forecast is for summer, followed by fall, winter, and spring. This happens for all configurations L50, L75, L100. The lightning forecast performance varies between sea and land; the analysis of the Taylor diagram shows better performance over the land than over the sea. This result shows that the convection is better simulated over the land than over the sea, where the effect of topography, partially represented by the model, may focus the convection on specific areas. The result of this study shows that lightning forecast with the dynamic lightning scheme can be performed with success in Italy; nevertheless, a careful inspection of the forecast performance is necessary for tuning the scheme to the specific purpose.

Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may some some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex center and a deep warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure). In this study the satellite measurements from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used to analyze the precipitation structure of the most intense Mediterranean Hurricane (Medicane) on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020. Two GPM-CO overpasses, available during Ianos development and tropical-like cyclone (mature) phase, are analyzed in detail. GPM Microwave Imager (GMI) measurements are used to carry out a comparative analysis of the medicane precipitation structure and microphysics processes between the two phases. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane mature phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Substantial difference in the rainband precipitation structure is observed, with deeper convection and stronger updraft features during development then at the mature phase, when also shallow precipitation/warm rain processes are observed in the inner region around the medicane eye. These features play a key role to explain the substantial drop in lightning activity during Ianos mature phase. Graupel-ice electrification process is inhibited due to the combined effect of strong horizontal wind and the observed limited growth of graupel. Starting from the detailed analysis of Ianos, a comparative study among the medicanes occurred during the GPM era is carried out. The goal is to extract common features from PMW measurements characterizing the different stage of medicanes’ evolution. The study demonstrates the value of the GPM-CO not only to characterize medicane precipitation structure and microphysics processes and convection strength with unprecedented detail, but also to provide evidence of tropical-like characteristics and of similarities with tropical cyclones for those medicanes undergoing tropical-like transition during their mature phase.

In this chapter, we evaluate the impact of lightning data assimilation (LDA) on the very short-term precipitation forecast using the Regional Atmospheric Modeling System at the Institute of Atmospheric Sciences and Climate meteorological model. Two different methods for LDA are applied: nudging and 3D-Var. LDA impact on the precipitation forecast is studied considering different cases and applications. In the beginning, two case studies (one missed and one well predicted by the control forecast without LDA) are analyzed, and a positive impact of LDA is obtained for both cases. For a statistically robust evaluation, results of the impact of LDA on the precipitation forecast for a whole year are shown. LDA performance shows a seasonal variability that matches lightning distribution in different seasons, with larger improvement in summer and fall, followed by spring and winter. Finally, two important aspects of LDA are studied considering 10 days of moderate–intense precipitation over Italy. First, the sensitivity of the rainfall forecast to the lower boundary for LDA application is investigated, considering two different levels, 0°C and lifting condensation level (LCL). Choosing LCL gives better performances, since it produces more hits and less false alarms. Second, the impact of LDA at different forecast ranges, that is, 3, 6, 9, and 12 h, is examined. LDA performance decreases as the forecast time increases, showing a noticeable improvement for the 3- and 6-h forecast, while a small and a negligible effect is obtained, respectively, for the 9- and 12-h forecast.

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) was launched on 27th February, 2014. It carries, for the first time, a Dual-frequency Precipitation Radar (DPR) designed to provide insights into the 3-D structure of precipitating clouds and rain intensity by using its Ka- and Ku-band frequencies. In addition to characterize the 3-D structure of precipitation, the DPR is used as calibrator for the GPM Microwave Imager (GMI). Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide, among the others, particle-size distribution (PSD) parameters (namely the mass-weighted mean diameter Dm and the normalized intercept parameter Nw), as well as precipitation rates. This book chapter focuses on reliability of the PSD parameters (limited to rainfall events, in this case we'll talk about of DSD - drop size distribution) over the Mediterranean area by taking as reference the DSD parameters estimated by ground-based radar measurement. Before of this, an inter-comparison between the SF and DF DPR outputs considering five years of data from GPM-CO mission is carried out. The goal is to investigate the reliability of SF-based products by assessing their quality compared to the DF-based ones, treated as a reference.

In this paper, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (very short-term forecast, 3 h in this study) for two severe weather events that occurred in Italy. The first case refers to a moderate and localized rainfall over central Italy that occurred on 16 September 2017. The second case occurred on 9 and 10 September 2017 and was very intense and caused damages in several geographical areas, especially in Livorno (Tuscany) where nine people died. The first case study was missed by several operational forecasts, including that performed by the model used in this paper, while the Livorno case was partially predicted by operational models. We use the RAMS@ISAC model (Regional Atmospheric Modelling System at Institute for Atmospheric Sciences and Climate of the Italian National Research Council), whose 3D-Var extension to the assimilation of radar reflectivity factor is shown in this paper for the first time. Results for the two cases show that the assimilation of lightning and radar reflectivity factor, especially when used together, have a significant and positive impact on the precipitation forecast. For specific time intervals, the data assimilation is of practical importance for civil protection purposes because it changes a missed forecast of intense precipitation (≥40 mm in 3 h) to a correct one. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its usefulness is partially reduced by the increase in false alarms, especially when both datasets are assimilated.

On September 14th, 2020 a depression originated on the Libyan coasts generated a Mediterranean tropical-like cyclone (hereafter referred to as Medicane Ianos), which moved northward until it hit, with its northernmost cloud bands, the southern Italian coasts and finally bent towards Greece, where it made landfall on September 18th, 2020. Heavy precipitation and flash flooding were reported, associated to huge damages to railway, houses, and four casualties.The correct prediction, as much as possible, of the trajectory and the intensity of these events is fundamental to prevent risks to infrastructures, natural landscapes, and people. One of the ways to evaluate the performances of the numerical weather prediction models is the comparison with satellite measurements. In particular, the mature phase of Medicane Ianos, as predicted by the weather research and forecasting (WRF) model, has been compared, for the first time, with the Global Precipitation Measurement mission Core Observatory (GPM-CO) active and passive measurements. Different microphysics schemes were used in order to investigate which is the most suitable to achieve the best forecast of Medicane Ianos considering different parameters as depression localization, reflectivity, rainfall rate, integrated liquid and ice content. The results show that all the schemes identified the precipitation bands structure of the Medicane overestimating the vertical extent of the convective structures. At the same time, all the schemes predicted an excessive columnar ice water content if compared to the one estimated from satellite measurements. It has to be highlighted that the overestimation is marked on the western precipitation bands of the Medicane eye, while a better agreement is obtained for the northern bands. Similar results are obtained for columnar liquid water content, even if the quantitative estimation is closer to the GPM measurements. Finally, all the schemes located the Medicane circulation center further north-west of its actual position.

Unique spaceborne measurements of the three-dimensional structure of convective clouds producing terrestrial gamma ray flashes (TGFs) were performed using both active and passive microwave sensors on board the Global Precipitation Measurement (GPM)-Core Observatory satellite, finding coherent features for nine TGF-producing storms. The delineation of cloud structure using the radar reflectivity factor shows convective cells with significant vertical development and thick layers with high ice content. Compared to other cumulonimbus clouds in the tropics, the TGFs counterparts have higher reflectivity values above 3 and 8 km altitude showing in all cases a cumulonimbus tower and the TGFs locations are very close, or coincident, to these high Z columns, where reflectivity exceeds 50 dBz. Using the GPM Microwave Imager radiometer, most thunderstorms show a very strong depression of polarization corrected temperature (PCT) at channel 89 GHz, indicating a strong scattering signal by ice in the upper cloud layers. At channel 166 GHZ, the difference between vertical and horizontal brightness temperature signal always returns positive values, from 0.2 up to 13.7 K indicating a complex structure with randomly/vertically oriented ice particles. The PCT was used to characterize the analyzed storms in terms of hydrometeor types, confirming in 7/9 cases a high likelihood of hail/graupel presence. To perform analysis on the TGFs parent flashes, radio atmospherics data from the Earth Networks Total Lightning Network lightning network were used. Waveform data indicate that all cases are intra-cloud events and TGFs typically take place during the peak of flash rate production. Finally, the analysis of the most intense event is shown.

This study analyses the impact of total lightning data assimilation on cloud-resolving short-term (3 and 6 h) precipitation forecasts of three heavy rainfall events that occurred recently in Italy by providing an evaluation of forecast skill using statistical scores for 3-hourly thresholds against observational data from a dense rain gauge network. The experiments are performed with two initial and boundary conditions datasets as a sensitivity test. The three rainfall events have been chosen to better represent the convective regime spectrum: from a short-lived and localised thunderstorm to a long-lived and widespread event, along with a case that had elements of both. This analysis illustrates the ability of the lightning data assimilation (LDA) to notably improve the short-term rainfall forecasts with respect to control simulations without LDA. The assimilation of lightning enhances the representation of convection in the model and translates into a better spatiotemporal positioning of the storm systems. The results of the statistical scores reveal that simulations with LDA always improve the probability of detection, particularly for rainfall thresholds exceeding 40 mm/3 h. The false alarm ratio also improves but appears to be more sensitive to the model initial and boundary conditions. Overall, these results show a systematic advantage of the LDA with a 3-h forecast range over 6-h.

In this article we report the first investigation over time of the atmospheric conditions around TGFs occurrence, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalyses data. The goal is to understand which characteristics are favourable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurred at a distance lower than 45 km from a GPS sensor, were analysed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN) was used in order to identify any links and correlations with TGF occurrence and PWV trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurred pattern in which an increase of PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors, described TGFs producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data give an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalyses data showed that TGFs mainly occur in convective environment with not exceptional values with respect to the monthly average value of parameters measured in the same location. Moreover the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphology. This study provided indications on the dynamics of convective systems linked to TGFs and will certainly help refine our understanding on their production highlights a potential approach through the use of GPS data to explore the lightning activity trend and the TGFs occurrence.

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.