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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.

In this article, we report the first investigation over time of the atmospheric conditions around terrestrial gamma-ray flash (TGF) occurrences, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalysis data. The goal is to understand which characteristics are favorable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurring at a distance lower than 45 km from a GPS sensor, were analyzed 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 precipitable water vapor (PWV) trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurring pattern in which an increase in 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 TGF-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 provide an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalysis data showed that TGFs mainly occur in convective environments with unexceptional values with respect to the monthly average value of parameters measured at 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 morphologies. This study provides indications on the dynamics of con-vective systems linked to TGFs and will certainly help refine our understanding of their production, as well as highlighting a potential approach through the use of GPS data to explore the lightning activity trend and TGF occurrences. publishedVersion

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.

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

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.

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.

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.

A new precipitation retrieval algorithm for the AMSR2 is described. The algorithm is based on the cloud dynamics and radiation database (CDRD) Bayesian approach and represents an evolution of the previous version applied to SSMIS observations, and used operationally within the EUMETSAT H-SAF program. This new product presents as main innovation the use of a very large database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA GPM-CO launched on February 28, 2014. The other new aspects are: 1) a new rain-/no-rain screening approach; 2) use of EOF and CCA for dimensionality reduction; 3) use of new ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; and 4) development and implementations of modules for computation time minimization. A verification study for case studies over Italy and for coincident AMSR2/GPM-CO observations over the MSG full disk area has been carried out. Results show remarkable AMSR2 capabilities for RR retrieval over ocean (for RR > 0.1 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that the new CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. The algorithm is also able to handle an extremely large observational database available from GPM-CO and to provide rainfall estimate with minimum latency, making it suitable for NRT hydrological and operational applications.

In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

The accurate estimation of rainfall from remote sensing is of paramount importance for many applications as, for instance, the mitigation of natural hazards like floods, droughts, and landslides. Traditionally, microwave observations in the frequency between 10 and 183 GHz are used for estimating rainfall based on the direct interaction of radiation with the hydrometeors within precipitating clouds in a so-called top-down approach. Recently, a bottom-up approach was proposed that uses satellite soil moisture products derived from microwave observations (

Continuous estimates of the vertical integrated precipitable water vapor content from the tropospheric delay of the signal received by the antennas of the global positioning system (GPS) are used in this paper, in conjunction with the measurements of the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI) radiometer and with the lightning activity, collected here by the ground-based lightning detection network (LINET), in order to identify links and recurrent patterns useful for improving nowcasting applications. The analysis of a couple of events is shown here as an example of more general behavior. Clear signs appear before the peak of lightning activity on a timescale from 2 to 3 h. In particular, the lightning activity is generally preceded by a period in which the difference between SEVIRI brightness temperature (TB) at channel 5 and channel 6 (i.e., ∆TB) presents quite constant values around 0 K. This trend is accompanied by an increase in precipitable water vapor (PWV) values, reaching a maximum in conjunction with the major flash activity. The results shown in this paper evidence good potentials of using radiometer and GPS measurements together for predicting the abrupt intensification of lightning activity in nowcasting systems.

International audience; In 1999, the first sprites were observed above European thunderstorms using sensitive cameras. Since then, Eurosprite campaigns have been conducted to observe sprites and other transient luminous events (TLEs), expanding into a network covering large parts of Europe and coastal areas. In 2009 through 2013, the number of optical observations of TLEs reached a peak of 2000 per year. Because of this unprecedented number of European observations, it was possible to construct a climatology of 8394 TLEs observed above 1018 thunderstorm systems and study for the first time their distribution and seasonal cycle above Europe and parts of the Mediterranean Sea. The number of TLEs per thunderstorm was found to follow a power law, with less than 10 TLEs for 801 thunderstorms and up to 195 TLEs above the most prolific one. The majority of TLEs were classified as sprites, 641 elves, 280 halos, 70 upward lightning, 2 blue jets and 1 gigantic jet. The climatology shows intense TLE activity during summer over continental areas and in late autumn over coastal areas and sea. The two seasons peak, respectively, in August and November, separated by March and April with almost no TLEs, and a relative minimum around September. The observed TLE activity, i.e. mostly sprites, is shown to be largely consistent with lightning activity, with a 1/1000 of observed TLE-to-lightning ratio in regions with most observations. The overall behaviour is consistent among individual years, making the observed seasonal cycle a robust general feature of TLE activity above Europe

This work refers to an interdisciplinary study on the archaeological site of Pyrgi, an Etruscan harbour still under excavation, located on the Lazio's coast in Santa Severa, in the province of Rome. The site in question is subject to frequent flooding which compromises its accessibility and delays the archaeological excavation operations. The study is based on the combined use of geomatic technologies, meteorological and climatic models, and hydrogeological knowledge of the examined site, to have a global view of the hazard to which it is exposed. Different geomatic techniques at different scales are used in the analysis. Large scale surveys are carried out to define the water networks and to monitor the site using satellite images. On a small scale, drone photogrammetry techniques are used to assess the morphology of the territory and eventual protection from natural hazards present in the site. Using these images, a detailed digital surface model (DSM) has been generated. The objective of the research is to assess the main cause of the floods and the time the water stays in the site and to determine if the floods are periodic phenomena over time or random events. The study was conducted using images captured by Sentinel 2 satellites processed at level 2-A. These images enabled the identification of the flooding periods of the site for the years of monitoring. The study was conducted by comparing the captured images with rainfall data, paying attention to extreme weather phenomena that occurred from 2012 to date. The rainfall data are provided by the National Department of Civil Protection to CNR-ISAC by an agreement between the two institutions. The same images have been compared with the wind data recorded by the anemometer located in the Civitavecchia harbour and the wave height data available from ERA5 reanalysis. Knowledge of the main cause of the floods and a possible periodicity will allow to plan correct conservation of the site through specific protection measures designed according to the hazards to which it is exposed.

Since their discovery, Terrestrial Gamma-ray Flashes (TGFs) exhibited an evident correlation with thunderstorms and lightning activity. The fleeting nature of these events and the heavy absorption of gamma-rays in the lowest atmospheric layers severely hampers the observation of this phenomenon, making us reveal just a small fraction of a probably much wider population. As each thunderstorm produces a large amount of lightning discharges during its lifetime, it is reasonable that even a large amount of TGFs are produced during the same event. However, detection of multiple TGFs coming from the same storm is difficult to perform, as it requires the constant monitoring of a spatially limited geographic region: this is not an easy task to perform for satellites on high-inclination orbits, that make them experience non-negligible latitudinal shifts at each orbital passage over a certain region, preventing the monitoring of a limited geographic region throughout successive overpasses. In this perspective, the quasi-equatorial (2.5∘) orbit of the AGILE satellite ensures a minimal latitudinal shift when flying over the same region at successive passages, allowing for the follow-up of thunderstorms in time. We exploit this feature of the AGILE satellite to search for multiple TGFs coming from the same geographic region and, in particular, from the same thunderstorm. We carry out this search on the AGILE TGF database (2009-2016), ending up with a sample of 79 systems producing more than one TGF, both during the same overpass and up to four overpasses after. Data acquired by geostationary meteorological satellites and cross-correlation with radio sferics detected by WWLLN are used to support this investigation. The AGILE satellite for the first time clearly establishes the multiple occurrence of TGFs from convective thunderstorms, both on timescales of minutes to several hours.

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, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (Very Short-term Forecast, 3 hours in this study) for two relevant case studies occurred over Italy. The first case refers to a moderate localised rainfall over Central Italy happened on 16 September 2017. The second case, occurred on 09 and 10 September 2017, was very intense and caused damages in several parts of Italy, while nine people died around Livorno, in Tuscany. The first case study was missed by most operational forecasts over Italy, 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. 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. The improvement compared to the control model, not assimilating lightning and radar reflectivity factor, is systematic because occurs for all the Very Short-term Forecast (VSF, 3h) of the events considered. For specific time intervals, the data assimilation is of practical importance for Civil Protection purposes because it transforms a missed forecast of intense precipitation (> 40 mm/3h) in a correct forecast. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its impact is reduced by the increase of the false alarms in the forecast assimilating both types of data.

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using “ad hoc„ classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters.

This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature, and graupel mixing ratio. The methodology is set up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to 20 cases that occurred in fall 2012, which were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of rain gauges over Italy, which is the target area of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of convection by lightning data assimilation improves the precipitation forecast at fine scales (meso-β). The application of the methodology to 20 cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the bias. The probability of detection (POD) increases by 3–5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on 3 July 2005, starting activity on 1 September 2005. The Italian Meteorological Service serves as Leading Entity on behalf of twelve European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations, while other products are based on the assimilation of satellite measurements/products into numerical models. In addition to product development and generation, H-SAF includes a product validation program and a hydrological validation program that are coordinated, respectively, by the Italian Department of Civil Protection and by the Polish Institute of Meteorology and Water Management. The National Center of Aeronautical Meteorology and Climatology (CNMCA) of the Italian Air Force is responsible for operational product generation and dissemination. In this paper we describe the H-SAF precipitation algorithms and products, which have been developed by the Italian Institute of Atmospheric Sciences and Climate (in collaboration with the international community) and by CNMCA during the Development Phase (DP, 2005–2010) and the first Continuous Development and Operations Phase (CDOP-1, 2010–2012). The precipitation products are based on passive microwave measurements obtained from radiometers onboard different sun-synchronous low-Earth-orbiting satellites (especially, the SSM/I and SSMIS radiometers onboard DMSP satellites and the AMSU-A + AMSU-B/MHS radiometer suites onboard EPS-MetOp and NOAA-POES satellites), as well as on combined infrared/passive microwave measurements in which the passive microwave precipitation estimates are used in conjunction with SEVIRI images from the geostationary MSG satellite. Moreover, the H-SAF product generation and dissemination chain and independent product validation activities are described. Also, the H-SAF program and its associated activities that currently are being carried out or are planned to be performed within the second CDOP phase (CDOP-2, 2012–2017) are presented in some detail. Insofar as CDOP-2 is concerned, it is emphasized that all algorithms and processing schemes will be improved and enhanced so as to extend them to satellites that will be operational within this decade – particularly the geostationary Meteosat Third Generation satellites and the low-Earth-orbiting Core Observatory of the international Global Precipitation Measurement mission. Finally, the role of H-SAF within the international science and operations community is explained.

The assessment of the actual observational capabilities of snowfall by spaceborne microwave radiometers is crucial to develop and improve precipitation retrieval algorithms. Exploiting coincident spaceborne active and passive microwave sensor datasets can effectively enhance our understanding of high-frequency microwave channels sensitivity to snowfall. This study illustrates the results of the analysis of matched Global Precipitation Measurement (GPM) Microwave Imager (GMI) and CloudSat Cloud Profiling Radar (CPR) snowfall observations (mainly found at latitudes between 55° and 65°) providing insights on GMI multi-frequency signals associated with different snowfall types. These findings are used to develop a new algorithm to retrieve snow water path associated to surface snowfall from GMI multichannel measurements.

At the end of March 2015 the onboard software configuration of the Astrorivelatore Gamma a Immagini Leggero (AGILE) satellite was modified in order to disable the veto signal of the anticoincidence shield for the minicalorimeter instrument. The motivation for such a change was the understanding that the dead time induced by the anticoincidence prevented the detection of a large fraction of Terrestrial Gamma-Ray Flashes (TGFs). The configuration change was highly successful resulting in an increase of one order of magnitude in TGF detection rate. As expected, the largest fraction of the new events has short duration (

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades, however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Para&iacute, ba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Para&iacute, ba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.

CloudSat spaceborne radar snowfall retrievals using two different methodologies – the 2C-SNOW-PROFILE (2C-SNOW) CloudSat product and the combined Kulie and Bennartz (2009) technique with the Hiley et al. (2011) reflectivity (Z) to snowfall rate (S) conversion (KBH) - are compared over Antarctica and surrounding Southern Ocean environments. KBH algorithm sensitivity tests are performed to demonstrate how retrievals are affected by algorithm assumptions (e.g., vertical reflectivity continuity test, the choice of near surface bin used to make surface snowfall rate retrievals, and temperature filters). These algorithm components are found to be detrimental to snowfall detection over this region by significantly reducing the snowfall population compared to 2C-SNOW, especially over ocean regions prone to ERA-interim indicated convective snow. After accounting for key algorithm differences, 2C-SNOW mean annual snowfall rates are systematically higher than KBH due to the Z -S relationship adopted. Spatial annual mean snowfall accumulation differences between the two datasets are minimized over interior Antarctica, but large differences are observed over the ocean. 2C-SNOW Z -S relationships for all snowfall events (Z = 10.9 S 1.3 ), over ocean and sea ice (Z = 8.2 S 1.3 ), escarpments and Antarctic coastal areas - below 2000 m a.s.l. (Z = 6.7 S 1.4 ), and Antarctic plateau - above 2000 m a.s.l. (Z = 5.5 S 1.6 ) are also derived. A multi-year 2C-SNOW mean annual snowfall analysis is also provided, and comparisons with ERA-Interim snowfall datasets show similar spatial patterns, but magnitude differences over oceans are observed. A monthly 2C-SNOW and acoustic depth gauge analysis is provided to demonstrate qualitative trends in snowfall accumulation between the respective datasets.

The estimation of the precipitable water vapour content (W) with high temporal and spatial resolution is of great interest to both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun–sky radiometers, or sun photometers, operating in several international networks, allowed the development of automatic estimations of W from these instruments with high temporal resolution. However, the great problem of this methodology is the estimation of the sun-photometric calibration parameters. The objective of this paper is to validate a new methodology based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance at 940 nm are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site. To obtain the calibration parameters some simultaneously seasonal measurements of W, from independent sources, taken over a large range of solar zenith angle and covering a wide range of W, are needed. In this work yearly GNSS/GPS datasets were used for obtaining a table of photometric calibration constants and the methodology was applied and validated in three European ESR-SKYNET network sites, characterized by different atmospheric and climatic conditions: Rome, Valencia and Aosta. Results were validated against the GNSS/GPS and AErosol RObotic NETwork (AERONET) W estimations. In both the validations the agreement was very high, with a percentage RMSD of about 6, 13 and 8 % in the case of GPS intercomparison at Rome, Aosta and Valencia, respectively, and of 8 % in the case of AERONET comparison in Valencia. Analysing the results by W classes, the present methodology was found to clearly improve W estimation at low W content when compared against AERONET in terms of % bias, bringing the agreement with the GPS (considered the reference one) from a % bias of 5.76 to 0.52.

Precipitation retrievals exploiting the available passive microwave (PMW) observations by cross-track and conically scanning satellite-borne radiometers in the Global Precipitation Measurement (GPM) mission era are used to monitor and characterize heavy precipitation events that occurred during the Fall 2014 in Italy. Different physically based PMW precipitation retrieval algorithms are used: the Cloud Dynamics and Radiation Database (CDRD) and Passive microwave Neural network Precipitation Retrieval (PNPR), used operationally in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF), and the National Aeronautics and Space Administration (NASA) Goddard PROFiling algorithm (GPROF). Results show that PMW precipitation retrievals from theGPMconstellation of radiometers provide a reliable and quantitative description of the precipitation (instantaneous and on the daily scale) throughout the evolution of the precipitation systems in the Mediterranean region. The comparable relative errors among gauges, radar, and combination of radiometer overpasses legitimize the use of PMW estimates as a valuable and independent tool for monitoring precipitation. The pixel-based comparison with dual-polarization radars and raingauges indicates the ability of the different sensors to identify different precipitation areas and regimes (0.60 < POD < 0.76; 0.28 < FAR < 0.45; 0.42 < ETS < 0.59;−1.6 mm/h < ME < 1.1 mm/h, with values depending on the radiometer and on the precipitation product). This is particularly relevant in the presence of complex orography in proximity of coastal areas, as for the analyzed cases. The different characteristics of the radiometers (i.e., viewing geometry, spatial resolution, channel assortment) and of retrieval techniques, as well as the limitations of the ground-based reference datasets, are taken into consideration in the evaluation of the accuracy and consistency of the retrievals.

Cloud electrification and related lightning activity in thunderstorms have their origin in the charge separation and resulting distribution of charged iced particles within the cloud. So far, the ice distribution within convective clouds has been investigated mainly by means of ground-based meteorological radars. In this paper we show how the products from Cloud Profiling Radar (CPR) on board CloudSat, a polar satellite of NASA's Earth System Science Pathfinder (ESSP), can be used to obtain information from space on the vertical distribution of ice particles and ice content and relate them to the lightning activity. The analysis has been carried out, focusing on 12 convective events over Italy that crossed CloudSat overpasses during significant lightning activity. The CPR products considered here are the vertical profiles of cloud ice water content (IWC) and the effective radius (ER) of ice particles, which are compared with the number of strokes as measured by a ground lightning network (LINET). Results show a strong correlation between the number of strokes and the vertical distribution of ice particles as depicted by the 94 GHz CPR products: in particular, cloud upper and middle levels, high IWC content and relatively high ER seem to be favourable contributory causes for CG (cloud to ground) stroke occurrence.

On 5 September 2015 a violent hailstorm hit the Gulf and the city of Naples in Italy. The storm originated over the Tyrrhenian Sea dropping 7-10 cm diameter hailstones along its path. During its mature phase, at 08:47 UTC, the hailstorm was captured by one overpass of the Global Precipitation Measurement mission Core Observatory (GPM-CO) embarking the GPM Microwave Imager (GMI) and the Ka/Ku-band Dual-frequency Precipitation Radar (DPR). In this paper, observations by both GMI and DPR are thoroughly analyzed in conjunction with other spaceborne and ground-based measurements, to show how the GPM-CO integrates established observational tools in monitoring, understanding, and characterizing severe weather. Rapid-scan MSG SEVIRI images show an extremely rapid development, with 10.8 mu m cloud-top temperatures dropping by 65 K in 40 min down to 198 K. The Lightning NETwork registered over 37,000 strokes in 5 h, with intracloud positive stroke fraction increasing during the regeneration phases, when ground-based polarimetric radar and DPR support the presence of large graupel/hail particles. DPR Ku 40 dBZ and 20 dBZ echo top heights at 14 km and 16 km, respectively, indicate strong updraft and deep overshooting. GMI extremely low brightness temperatures (TBs) in correspondence of the convective core (158, 97, 67, and 87 K at 18.7, 36.5, 89 and 166 GHz) are compatible with the presence of massive ice particles. In two years of GPM global observations the storm ranks as fourth and first in terms of minimum 36.5 and 18.7 GHz (V-pol) TBs, respectively. This study illustrates GPM-CO sensing capabilities for characterizing the structure of such severe hailstorm, while providing observational evidence of its intensity and rarity, both globally and over the Mediterranean area. (C) 2017 Elsevier B.V. All rights reserved.

The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.