Una piattaforma open source per la navigazione indoor: applicazione alla facoltà di ingegneria civile e industriale dell'università di Roma "Sapienza"

“Il conferire struttura e identità all’ambiente è una capacità vitale propria di tutti gli animali dotati di movimento” (Lynch, 1964). È proprio da questo che prende spunto l’idea del progetto qui proposto, dal tentativo di riproporre su un foglio il territorio che si abita, dando specifico ordine agli elementi; in quanto è proprio l’assenza di smarrimento e la capacità di orientarsi a darci un senso di equilibrio e benessere. “La stessa parola smarrito significa, nella nostra lingua, molto di più che semplice incertezza geografica: essa porta con sé sfumature di vera tragedia. […] si potrebbe obiettare, che il cervello umano è meravigliosamente adattabile. […] eppure, anche il mare ha il sole e le stelle, i venti, le correnti, gli uccelli e il colore dell’acqua senza i quali la navigazione sarebbe impossibile.” (Lynch, 1964) La cartografia riveste un ruolo di fondamentale importanza per tutti i dati di tipo geografico, i dati cioè che presentano come caratteristica principale un attributo spaziale; i fenomeni descrivibili a partire dal loro posizionamento vengono detti georeferenziabili. La cartografia, pertanto, si occupa dell'archiviazione (e della rappresentazione) di dati georeferenziabili; essa mira, dunque, a fornire una conoscenza del territorio sia di tipo puntuale che generale, a sviluppare processi logici in funzione di relazioni e a fungere da supporto di base per pianificazione, progettazione e gestione del territorio. (Brovelli, 2000) È in questo contesto che si inserisce il progetto in oggetto, nel tentativo di fornire uno strumento utile e potente che consenta al singolo utente di orientarsi in tempo reale, e quindi supportando la vera e propria navigazione, in aree non caratterizzate da copertura GNSS. Il progetto qui riportato propone l’utilizzo di una piattaforma open source per supportare la navigazione indoor, e ne mostra un’applicazione negli ambienti della facoltà di ingegneria civile e industriale dell’università di Roma “Sapienza”."Granting structure and identity to the environment is a vital capacity of all animals with movement." (Lynch, 1964) It is precisely from this that the idea of the project proposed here is inspired, by the attempt to propose the territory that people dwell on a sheet, giving specific order to the elements; as it is the absence of loss and the ability to orient ourselves to give us a sense of balance and well-being. "The same word lost means in our language much more than simple geographical uncertainty: it brings with it shades of true tragedy. [...] One might object, that the human brain is wonderfully adaptable. [...] Still, even the sea has the sun and the stars, the winds, the currents, the birds and the color of the water without which the navigation would be Impossible." (Lynch, 1964) Cartography plays a fundamental role for all geographic data, that are data which have a spatial attribute as the main feature; The phenomena that can be described starting from their positioning are termed georeferenced. Cartography, therefore, deals with the storage (and representation) of georeferencing data; it aims to provide a knowledge of both local and general territory, to develop logic processes based on relationships and to serve as a base for planning, planning and management of the territory (Brovelli, 2000). It is in this context that the project is inserted in an attempt to provide a useful and powerful tool that allows a single user to navigate, in real time, in areas without GNSS coverage. The project proposes the use of an Open Source platform to support indoor navigation and shows an application in the environments of the Faculty of Civil and Industrial Engineering at the University of Rome "Sapienza"

Time evolution of storms producing terrestrial gamma-ray flashes using era5 reanalysis data, gps, lightning and geo-stationary satellite observations

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.publishedVersio

New applications and opportunities of GNSS meteorology

Water vapor content of the atmospheric low layer, up to about 18 km, known as troposphere or neutral atmosphere, affects GNSS (Global Navigation Satellite System) signals by lowering their propagation velocities with respect to vacuum. A diminished speed results in a time delay in the signal propagation along the satellite-receiver path, that multiplied by the vacuum speed of light adds an extra-distance to the satellite-receiver geometrical one. This delay defines a parameter which takes the name of Tropospheric Delay (TD) and consists of an Hydrostatic (HD) and aWet (WD) part. Anyway, if from the positioning point of view this delay is just a systematic error to be removed, it puts forward GNSS as a tool for the remote sensing of the troposphere water vapor content. The role of GNSS in meteorology is rapidly increasing; water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology [16] and GNSS data can be extremely useful for the purpose of a multi-instrumental investigation; indeed it was used to calibrate and validate other instruments [26] or methodologies [87], but probably one of the most impactful application is related to the assimilation of GNSS data into the meteorological models [8] [42] [19]. In this study, a global analysis of the role of the GPS data in the field of meteorology was carried out. A focus was kept on the assimilation of data in NWP Models, also according to the cooperation with the Institute of Atmospheric Sciences and Climate of National Research Council of Italy, which included this work in the frame of a collaboration\ud with the Department of Italian Civil Protection. The main aim of this thesis is to find parameters able to support the analysis and forecast of intense meteorological events. To do this, a comparative analysis was carried out between GPS outputs and other Precipitable Water Vapor (PWV) measurement instruments; results show great consistency between the data (St. Dev.1cm). Another test was performed on the assimilation in NWP Models, in particular RAMS Model; in this case it has been found a noticeable impact (20-30% improvement) on ZTD and IWV for short term forecast. As for the Near Real Time (NRT) processing, results obtained are encouraging with a St. Dev.< 1cm with respect to post-processing (PP). To sum up, results provide an overall assessment of the data quality obtained through GPS post-processing and a milestone for NRT processing, also in view of early warning systems

A Combined IR-GPS Satellite Analysis for Potential Applications in Detecting and Predicting Lightning Activity

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

Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets

Predicting extreme weather events in a short time period and their developing in localized areas is a challenge. The nowcasting of severe and extreme weather events is an issue for air traffic management and control because it affects aviation safety, and determines delays and diversions. This work is part of a larger study devoted to nowcasting rain and wind speed in the area of Malpensa airport by merging different datasets. We use as reference the weather station of Novara to develop a nowcasting machine learning model which could be reusable in other locations. In this location we have the availability of ground-based weather sensors, a Global Navigation Satellite System (GNSS) receiver, a C-band radar and lightning detectors. Our analysis shows that the Long Short-Term Memory Encoder Decoder (LSTM E/D) approach is well suited for the nowcasting of meteo- rological variables. The predictions are based on 4 different datasets configurations providing rain and wind speed nowcast for 1 h with a time step of 10 min. The results are very promising with the extreme wind speed probability of detection higher than 90%, the false alarms lower than 2%, and a good performance in extreme rain detection for the first 30 min. The configuration using just weather stations and GNSS data in input provides excellent performances and should be preferred to the other ones, since it refers to the pre-convective envi- ronment, and thus can be adaptable to any weather conditions

Multivariate Multi-Step Convection Nowcasting with Deep Neural Networks: The Novara Case Study

Severe weather events are constantly increasing over northern Italy impacting the air traffic of one of the major airports of Europe: Milano Malpensa. Monitoring and predicting extreme convection is very challenging especially when it develops locally in a short time range. This work is performed within two projects funded by the H2020 SESAR Programme, with the objective of nowcasting with high accuracy the strong weather events affecting the airport. We collected different types of data from 10 locations around the airport and developed an end-to-end nowcasting deep neural networks based model for each of these stations. We show in this paper the results that we obtained for Novara, the only station for which we have available weather stations, radar, GNSS and lightning

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

Atmospheric Phase Screens (APSs) derived from Interferometric Synthetic Aperture Radar (InSAR) observations contain the difference between the tropospheric watervapor- induced delay of two acquisition epochs, i.e., the slave and the master (or reference) epochs. Using estimates of the atmospheric state coming from independent sources, for example numerical models and/or Global Navigation Satellite System (GNSS) observations, the APSs can be transformed into absolute maps of Tropospheric Delay (Zenith Total Delay or ZTD), related to the columnar atmospheric water vapor content. In this work, a systematic comparison between various APS and ZTD products aims to determine a convenient strategy to go from APSs to InSAR-derived absolute ZTD maps, highlighting the uncertainties and approximations introduced in the entire processing. The main problem to solve is the evaluation of a sufficiently accurate highresolution master delay map. Different sources of data and two different approaches to derive the master are validated and compared to define the most suitable strategy for meteorological applications. Maps of ZTD obtained by an iterative interpolation of a global atmospheric circulation model values results in being more suited than those derived from the assimilation of GNSS observations into an NWP model. A time average approach to estimate the master map is more robust than the single epoch approach with respect to the choice of the master epoch. Still, the choice of a proper master epoch in the InSAR processing chain as well

Time evolution of storms producing terrestrial gamma-ray flashes using era5 reanalysis data, gps, lightning and geo-stationary satellite observations

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

Data assimilation of GPS-ZTD into the RAMS model through 3D-Var: preliminary results at the regional scale

The knowledge of water vapour distribution is a key element in atmospheric modeling and considerable information, also at the local scale, can be derived from the GPS-ZTD (global positioning system-Zenith total delay) data. This paper shows the assimilation of GPS-ZTD data into the RAMS@ISAC (Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate of the National Research Council) to improve the representation of the water vapour in the meteorological model. The data assimilation system is based on 3D-Var (three-dimensional variational assimilation system) and it is applied to a network of 29 receivers located within the Lazio Region, Central Italy. All collected data are processed using the PPP (precise point positioning) method through RTKLIB, an open source program package for GNSS (Global Navigation Satellite Systems) Positioning. Among the GPS receivers, three are single frequency receivers, able to acquire L1 frequency only, so that it is necessary a preliminary reconstruction of L2 synthetic observations, which is achieved by a new original ground-based augmentation strategy. Results show remarkably that the single frequency receivers can be used the same way as geodetic receivers. The RAMS@ISAC is run at 4 km horizontal resolution over central Italy and is nested, using one-way nesting, into a 10 km horizontal resolution run of the same model. The experiment was performed along to two months, from 28 July to 28 September 2017. Results show that the GPS-ZTD data, assimilated by 3D-Var, have an important impact on the analysis of the water vapour field and the RMSE of ZTD and IWV (vertically integrated water vapour) is roughly halved for the analysis compared to the background. The impact of the GPS-ZTD data assimilation is also evaluated for the very short term (VSF) forecast (1–3 h), obtaining an improvement of the ZTD and IWV RMSE for all three hours of forecast