A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous event are analysed and go back to the presence of tropical cyclone Danielle in the eastern Atlantic. The performance of the weather research and forecasting (WRF) model using lightning data assimilation (LDA) is studied in this case by comparing the forecast with the control forecast without lightning data assimilation. The forecast performance is evaluated for precipitation and lightning. The case was characterised by four intense 3-h (3 h) periods. The forecasts of these four 3-h phases are analysed in a very short-term forecast (VSF) approach, in which a 3 h data assimilation phase is followed by a 3 h forecast. A homemade 3D-Var is used for lightning data assimilation with two different configurations: ANL, in which the lightning is assimilated until the start of the forecasting period, and ANL-1H, which assimilates lightning until 1 h before the 3 h forecasting period. A sensitivity test for the number of analyses used is also discussed. Results show that LDA has a significant and positive impact on the precipitation and lightning forecast for this case.

In the H2020 project "Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM" (SINOPTICA), an air traffic controller support system was extended to organize approaching traffic even under severe weather conditions. During project runtime, traffic days with extreme weather events in the Po Valley were analyzed, an arrival manager was extended with a module for 4D diversion trajectory calculation, two display variants for severe weather conditions in an air traffic controller primary display were developed, and the airport Milano Malpensa was modelled for an air traffic simulation. On the meteorological side, three new forecasting techniques were developed to better nowcast weather events affecting tactical air traffic operations and used to automatically organize arrival traffic. Additionally, short-range weather forecasts with high spatial resolution were elaborated using radar-based nowcasting and a numerical weather prediction model with data assimilation. This nowcast information was integrated into the extended arrival manager for the sequencing and guiding of approaching aircraft even in adverse weather situations. The combination of fast and reliable weather nowcasts with a guidance support system enables severe weather diversion coordination in combination with a visualization of its dynamics on traffic situation displays.

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios.

The study of natural disasters has become increasingly important in recent years as the frequency and impact of such events on society have risen. Italy, which has the largest number of sites on the World Heritage List, offers many examples of interactions between atmospheric phenomena and cultural heritage. The research presented here aimed to investigate the potential of one of these sites, Alberobello in the Apulia region, to respond to the stresses induced by intense weather phenomena that occurred in August 2022. Data from conventional and nonconventional sensors were employed to characterize the event. During previous studies, regions prone to meteorological risk were identified based on long-term model analyses. According to these studies, the marked area resulted in a region sensitive to convective precipitation and thus represents an interesting case study. The weather event investigated caused flooding and damage in the Alberobello surroundings; however, the UNESCO site showed a positive response. We explored the reasons by consulting the literature to outline the site's peculiarities, especially its architectural features, building materials,and terrain morphology. The results revealed that the mutual relationship between the buildings and the environment and the dual role of cultural heritage are values that need to be protected as a resource for natural hazard mitigation.

We processed five years of measurements (2018-2022) of a vertically pointing radar MIRA 35c at the Milesovka meteorological observatory with the aim of analyzing the cloud structure of thunderstorms and comparing differences in measured data for cases when lightning discharges were observed very close to the radar position, and for cases when lightning discharges were observed at a greater distance from the radar position. The MIRA 35c radar is a Doppler polarimetric radar working at 35 GHz (Ka-band) with a vertical resolution of 28.9 m and a time resolution of approximately 2 s. For the analysis, we considered radar data whose radar reflectivity was at least 10 dBZ at 5 km or higher above the radar to ensure that there was a cloud above the radar. We divided the radar data into "near" data (a lightning discharge was registered up to 1 km from the radar position) and "far" data (a lightning discharge was registered from 7.5 to 10 km from the radar position). We compared the following quantities: (i) Power in co-channel (pow), (ii) power in cross-channel (pow-cx), (iii) phase in co-channel (pha), (iv) phase in cross-channel (pha-cx), (v) equivalent radar reflectivity (Ze), (vi) Linear Depolarization Ratio (LDR), (vii) co-polar correlation coefficient (RHO), (viii) Doppler radial velocity (V), (ix) Doppler spectrum width (RMS), and (x) Differential phase (Phi). Pow, pow-cx, pha, pha-cx, and V are basic data measured by the radar, while Ze, LDR, RHO, RMS, and Phi are derived quantities. Our results showed that the characteristics of the compared radar quantities are clearly distinct for "near" dataset from "far" dataset. Furthermore, we found out that there is a clear evolution close to the time of discharges of the observed radar quantities in the "near" dataset, which is not that obvious in the "far" dataset.

The increasing and extreme weather phenomena observed in the Mediterranean basin are only one aspect of the problem which has broader effects on population, structures and infrastructure. Each of these aspects is itself characterized by a wide variety of issues, which are increasingly leading studies toward a multidimensional assessment of impacts (economic, social and environmental). In this study, we focus on the impact related to the increase in extreme weather events in a specific area characterized by typical vernacular architecture: the "trabocchi" of the Italian Adriatic coast, whose identification as cultural heritage is the result of historical events and social dynamics closely linked to the collective imagination and for which inclusion as intangible cultural heritage in the UNESCO World heritage List has been requested. The weather event investigation was performed considering both long-term large-scale (using the ERA5 dataset) analysis and short-term small-scale (models and ground-based sensors) analysis. The results provide an overview of the event dynamics and enhanced understanding of the area's vulnerability factors to extreme weather phenomena, as well as emphasized the need, in order to protect the integrity of the asset, to study environment changes and to plan concrete actions aimed at conservation, including social actions, to mitigate the problem.

The regions close to the sea are often hit by meteorological systems that generate over the sea and then are advected towards the land. These systems impact the activities over the sea and is it important to predict their occurrence for the safety of the people as well as for the best prediction of ship-routes. The lower number of meteorological observations over the sea compared to the land and the absence of the orographic triggering mechanism, makes prediction of these storms difficult. Satellite observations are very important in this framework because they provide data over both land and sea that can help the prediction of convective storms. The AEROMET project (AEROspatial data assimilation for METeorological weather prediction) aims to assimilate the rain-rate estimated from satellite observations into the Numerical Weather Prediction (NWP) Weather Research and Forecasting (WRF) model to improve the prediction of convective meteorological systems, especially those originating over the sea. The method to assimilate the rain-rate is straightforward: given the best estimate of the rain-rate, it is assimilated in the model through 3D-Var with a simple cloud model. Two examples, occurred on 10 December 2021 and on 15 February 2022, show the feasibility of the method, nevertheless many cases must be studied to quantify the impact of the assimilation of satellite observed rain-rate on the precipitation forecast.

Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field simulation. The overarching goal is to pave the way for an easy and automatic communication of the heavy rainfall warning derived by the nowcasting procedure. The results obtained applying the SWING algorithm over a case study of 22 days in the fall 2019 season suggest that the algorithm could improve the predictive capability of a traditional deterministic nowcasting forecast system, keeping a useful forecast timing and thus integrating the current forecast procedures. Eventually, the main advantage of the SWING algorithm is also its very high versatility, since it could be used with any meteorological model also in a multi-model forecast approach.

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

Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the "dynamic lightning scheme" for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme.

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.

In recent years, interest in the study of natural disasters has grown considerably, linked both to the increase in the number of such events and to their effect on the territory. Statistical analyses reported in the scientific literature show that the context is constantly changing and that severe weather events are increasingly observed even at mid-latitudes. In this respect, Mediterranean area is a unique environment for the occurrence of atmospheric-related phenomena. Many studies have already been conducted with the aim of identifying the regions most prone to meteorological risk and, to this end, long-term analyses conducted by models are particularly indicative. In this study, we defined a methodology to identify which UNESCO sites are located in areas most exposed to such extreme events, the definition of which will be driven by climatological analyses. As an example we examined, by a multi-sensor analysis, a particularly interesting case study referring to the well-known UNESCO site of Alberobello, in Apulia (Italy).

Tropical-like cyclone (TLC or medicane) Ianos formed during mid-September 2020 over the Southern Mediterranean Sea, and, during its mature stage on days 17-18, it affected southern Italy and especially Greece and its Ionian islands, where it brought widespread disruption due to torrential rainfall, severe wind gusts, and landslides, causing casualties. This study performs a sensitivity analysis of the mature phase of TLC Ianos with the WRF model to different microphysics parameterization schemes and initial and boundary condition (IBC) datasets. Satellite measurements from the Global Precipitation Measurement Mission-Core Observatory (GPM-CO) dual-frequency precipitation radar (DPR) and the Advanced Scatterometer (ASCAT) sea-surface wind field were used to verify the WRF model forecast quality. Results show that the model is most sensitive to the nature of the IBC dataset (spatial resolution and other dynamical and physical differences), which better defines the primary mesoscale features of Ianos (low-level vortex, eyewall, and main rainband structure) when using those at higher resolution (~25 km versus ~50 km) independently of the microphysics scheme, but with the downside of producing too much convection and excessively low minimum surface pressures. On the other hand, no significant differences emerged among their respective trajectories. All experiments overestimated the vertical extension of the main rainbands and display a tendency to shift the system to the west/northwest of the actual position. Especially among the experiments with the higher-resolution IBCs, the more complex WRF microphysics schemes (Thompson and Morrison) tended to outperform the others in terms of rain rate forecast and most of the other variables examined. Furthermore, WSM6 showed a good performance while WDM6 was generally the least accurate. Lastly, the calculation of the cyclone phase space diagram confirmed that all simulations triggered a warm-core storm, and all but one also exhibited axisymmetry at some point of the studied lifecycle.

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.

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.

Most of the municipalities of the Italian territory are located in areas of high hydrogeological risk, i.e. exposed to flooding and landslides. Consequently, part of the existing cultural heritage on the national territory is located in areas subject to flood risk, which compromises the accessibility, preservation and integrity of cultural heritage. As an example, we consider a single flood event that occurred in southern Italy on November 11th and 12th, 2019, which mainly affected the city of Matera and its surroundings. This episode appears to be significant for the violence of the phenomenon that led to considerable quantities of water flowing inside the city, a UNESCO World Heritage Site, causing damage to buildings, including historical ones. The event has been analysed using both meteorology and geomatic technologies, to have an overview on spatial and temporal evolution of the phenomenon. Global Navigation Satellite System Zenith Total Delay (GNSS-ZTD) data obtained by receivers located around the city of Matera, were compared with measurements from ground-based devices (i.e. weather stations), Numerical Weather Prediction (NWP) models, and ERA5 reanalysis. To assess the extent of the flood and show the flooded areas, the images provided by the Sentinel-1 Synthetic Aperture Radar (SAR) were used, isolating and analyzing the images captured before and after the event. Finally, through a digital terrain model, developed using Agisoft Metashape software from satellite images, the morphology of Matera was recreated to evidence the areas of accumulation of water. Once all the information was obtained, the data correlated showed an overall view of the event.

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.

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.

Windy episodes in recent years are increasing in intensity and frequency, amplifying the risk for coastal cultural heritage. The increase in wind speed leads to an increase in the height of the waves, generating, as a result, violent sea storms. This will lead to an intensification of flooding phenomena that can give rise to erosion and chemical damage to cultural property located along the coast. In addition, rising temperatures and melting glaciers are the cause of rising sea levels, which can cause intermittent water flow to sites and/or permanent immersion of certain parts of the territory. The proposed study takes into consideration the archaeological area of Pyrgi, an Etruscan port located along the Lazio coast in the city of Santa Severa (Rome), still under excavation. The site in question is adjacent to the coast and has major flooding problems, particularly during autumn and winter. This phenomenon not only leads to both mechanical and chemical problems, but also causes problems during excavation operations. The work reported is highly interdisciplinary, and its final objective is to characterize the triggering conditions of the flooding phenomenon in order to adopt solutions and interventions to correct this problem. Through the images acquired by drone, related to the archaeological area, a digital terrain model (DTM) is generated, in order to represent the morphology of the area and then assess which are the areas of water retention. This DTM is integrated with the beach and bathymetry profile for the whole study. The bathymetric profile near the coast is in fact fundamental to define the evolution of the wave evolution on shallow water. To this aim, after the definition of the domain of interest and the proper grid, the numerical approach CFD (Computational Fluid Dynamics) was used, which is based on the technique of finished volumes based on RANS (Reynolds-averaged Navier-Stokes) approach with k-? turbulence closure, two-phase, not stationary. Tidal and field forcing conditions were evaluated on the basis of data acquired both from synoptic scale data (reanalysis ECMWF) and from experimental acquisition databases in sites near the Pyrgi area (anemometers, wave buoys). A simulation campaign was conducted for the different tidal conditions and wave heights and the critical flooding conditions were investigated, while assessing the water invasion in the different areas of the Pyrgi site. Future applications of this approach consist in the assessment of the effects of flooding, analysis and design of site protection solutions and the evolution of coastal erosion phenomena.

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) 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.