Geomatics Research & Development s.r.l.
Recent publications
Citation: Esbrí, L.; Rigo, T.; Llasat, M.C.; Biondi, R.; Federico, S.; Gluchshenko, O.; Kerschbaum, M.; Lagasio, M.; Mazzarella, V.; Milelli, M.; et al. Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management. Abstract: 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 bathymetry is the most superficial layer of the Earth’s crust on which it is possible to perform direct measurements. However, it is also well known that water covers more than 70% of the Earth’s surface, so an enormous expenditure of acquisition campaigns should be performed to produce a high-resolution map of this layer. Currently exploiting mainly commercial navigation routes, the sea floor coverage with shipborne sounding is only at 11%, and the remaining part is currently modeled by classical interpolation techniques or satellite-based gravity inversion methods. In the present work, a new method to refine bathymetry modeling based on the exploitation of global gravity field models is presented. In the proposed solution, once modeled and removed from the observed gravity field, the gravitational signals related to the deepest structures, a 3D Bayesian inversion algorithm is used to improve the actual knowledge of bathymetry. The proposed inversion method also enables limiting the solution to shipborne sounding measurements in such a way as to improve the seafloor grid where no local, high-quality information is available. Two test cases are discussed in the Mediterranean Sea region. Promising results are presented, opening the possibility of applying an analogous method to refine the bathymetry modeling at larger scales up to the global one.
The Earth’s crust is exceptionally important to understand the geological evolution of our planet and to access natural resources as minerals, critical raw materials, geothermal energy, water, hydrocarbons, etc.. However, in many regions of the world it is still poorly modelled and understood. Here we present the latest advance on three-dimensional modelling of the Mediterranean Sea crust based on freely available global gravity and magnetic field models. The proposed model, based on the inversion of gravity and magnetic field anomalies constrained by available a-priori information (such as interpreted seismic profiles, previous studies, etc.), provides, with an unprecedented spatial resolution of 15 km, the depths of the main modelled geological horizons (Plio-Quaternary, Messinian and Pre-Messinian sediments, crystalline crust and upper mantle), coherent with the known available constraints, together with the three-dimensional distribution of density and magnetic susceptibility. The inversion is carried out by means of a Bayesian algorithm, which allows to modify at the same time the geometries and the three dimensional distributions of density and magnetic susceptibility, always respecting the constraints introduced by the initial information. In addition to unveil the structure of the crust beneath the Mediterranean Sea, the present study also shows the informative content of freely available global gravity and magnetic models, thus putting the base for the development of future high resolution models of the Earth crust at global level.
Citation: Temme, M.-M.; Gluchshenko, O.; Nöhren, L.; Kleinert, M.; Ohneiser, O.; Muth, K.; Ehr, H.; Groß, N.; Temme, A.; Lagasio, M.; et al. Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager. Aerospace 2023, 10, 210. Abstract: 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.
This paper deals with the problem of geodetic monitoring of structures by means of permanent GNSS stations, with a focus on a specific project of monitoring a bridge by a small network of three stations. What is peculiar about this paper is that the stations used are endowed with low-cost GNSS receivers, and the data treated continuously cover a time-span of more than 4 years. The monitoring service GeoGuard, at work on the project, has proved to be reliable in terms of both hardware and software. The results display almost uniform accuracy at less than the 1 mm level for daily adjusted coordinates and at the level of ∼1–2 mm for hourly solutions. After a short review on the basics of positioning by GNSS phase observations, the error of the estimated coordinates is discussed in detail, and a procedure of warning/alarm is described. The experience in terms of hardware and software employed is then presented together with the results, which are mostly displayed in graphical form and with a few tables.
The use of potential field methods for geophysical exploration purposes is nowadays quite common: these techniques consent to retrieve geological knowledge over extended regions and can give complementary information where other invasive or expensive techniques, such as seismic acquisitions, fail (e.g. in the recovery of geometries of geological horizons beneath a thick salt layer). Recent dedicated satellite gravity and magnetic missions, such as GRACE, GOCE and SWARM together with the exploitation of offshore satellite altimetry and airborne/shipborne surveys, have paved the way to the realization of a variety of global models, characterized by spatial resolutions of about 4 km (both for gravity anomaly and lithosphere magnetic anomalies) and high‐accuracy, i.e. about 3‐5 mGal and 20 nT. These models are a valuable source of information to study the geological evolution and characterization of the lithosphere structure, especially at regional scale. In the present work some preliminary technical aspects related to the use of these models to perform 3D inversion are discussed, thus defining an empirical but rigorous procedure to setup gravity and magnetic inversion. In particular, we address the questions whether the classical planar approximation is acceptable for regional inversions or if a spherical one is required. We also provide guidance for choosing the best gravity functional (e.g. gravity anomalies or second radial derivative of the anomalous potential) and the optimal sizing of the 3D volume area to be modeled depending on the specific target investigated. The application of the proposed methods to the Mediterranean case study is also presented. This article is protected by copyright. All rights reserved
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.
Since its discovery in 1909, the Moho was routinely studied by seismological methods. However, from the 1950s, a possible alternative was introduced by gravimetric inversion. Thanks to satellite gravity missions launched from the beginning of the 21st century, a global inversion became feasible, e.g., leading to the computation of the GEMMA model in 2012. This model was computed inverting the GOCE second radial derivatives of the anomalous potential by a Wiener filter, which was applied in the spherical harmonic domain, considering a two-layer model with lateral and vertical density variations. Moreover, seismic information was introduced in the inversion to deal with the joint estimation/correction of both density and geometry of the crustal model. This study aims at revising the GEMMA algorithm from the theoretical point of view, introducing a cleaner formalization and studying the used approximations more thoroughly. The updates are on: (1) the management of the approximations due to the forward operator linearization required for the inversion; (2) the regularization of spherical harmonic coefficients in the inversion by proper modelling the Moho signal and the gravity error covariances; (3) the inclusion of additional parameters and their regularization in the Least Squares adjustment to correct the density model by exploiting seismic information. Thanks to these updates, a significant improvement from the computational point of view is achieved too, thus the convergence of the iterative solution and the differences with respect to the previous algorithm can be assessed by closed-loop tests, showing the algorithm performance in retrieving the simulated “true” Moho.
Potential fields methods, based on the exploitation of gravity and magnetic fields, are among the most important methods to recover fundamental information on the Earth crust structure at global, regional and local scales. The bottleneck for this kind of geophysical methods is often represented by the development of ad-hoc techniques to fully exploit the available data. In fact, each different technique can observe the effect of a single property of the subsurface and when we want to estimate this property from the observed field (the so-called inverse problem), several problems such as non-uniqueness and instability arise. A possible solution to these problems consists in jointly inverting, in a consistent way, different observed fields, possibly also incorporating all the available geological constraints. In the current work, we present an innovative Bayesian algorithm aimed at performing a full 3D joint inversion of gravity and magnetic fields constrained by geological a-priori qualitative information. The algorithm is tested on a real-case scenario, namely, a local study to estimate a complete 3D model of the Oka carbonatite complex. This complex is a composite pluton in Quebec (Canada), important for mining operations related to critical raw material such as Niobium and other rare earth. This example shows the reliability of the developed inversion algorithm and gives hints on the fundamental role that potential fields can play in mining activities.
Dams are one of the most important engineering works of the current human society, and it is crucial to monitor and obtain analytical data to log their conditions, predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. In this context, GNSS-based point displacement monitoring is nowadays a consolidated technique that is able to provide daily millimeter level accuracy, even with less sophisticated and less expensive single-frequency equipment. If properly designed, daily records of such monitoring systems produce time series that, when long enough, allow for an accurate reconstruction of the geometrical deformation of the structure, thus guiding semi-automatic early warning systems. This paper focuses on the procedure for the GNSS time series processing with a statistical approach. In particular, real-world times series collected from a dam monitoring test case are processed as an example of data filtering. A remove–restore technique based on a collocation approach is applied here. Basically, it consists of an initial deterministic modeling by polynomials and periodical components through least squares adjustment and Fourier transform, respectively, followed by a stochastic modeling based on empirical covariance estimation and a collocation approach. Filtered time series are interpreted by autoregressive models based on environmental factors such as air or water temperature and reservoir water level. Spatial analysis is finally performed by computing correlations between displacements of the monitored points, as well as by visualizing the overall structure deformation in time. Results positively validate the proposed data processing workflow, providing useful hints for the implementation of automatic early warning systems in the framework of structural monitoring based on continuous displacement measurements.
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 the present study, a Bayesian gravity inversion algorithm is applied to infer a complete 3D density model of the crust in the region of the Wilkes Land. One of the main objective of this work is to provide information on the thickening/thinning of the crust beneath the basin or the amount and characteristics of sediment deposits in the area. After collecting all the latest available geophysical data and models of the study region, neglecting gravity derived information, a first 3D model was defined in terms of principal geological horizons and density distribution together with an estimate of its accuracy. Then, two gravity observations, namely gravity disturbances and second radial derivative of the anomalous potential, were jointly inverted in order to adjust the a-priori 3D model and obtain the so called a-posterior improved model, now coherent with gravity. The present work summarizes the principal results obtained within the inversion performed in the Wilkes Land region together with a sensitivity analysis to assess the reliability of the inverted 3D model. The results show a crustal thickness below the Wilkes Land higher than 25 km, characterized by higher densities, sedimentary basins that reach in some zones thicknesses of about 7 km and geometries below the TAM that suggest a large root of the mountains and lighter mantle densities.
The exploitation of gravity fields in order to retrieve information about subsurface geological structures is sometimes considered a second rank method, in favour of other geophysical methods, such as seismic, able to provide a high resolution detailed picture of the main geological horizons. Within the current work we prove, through a realistic synthetic case study, that the gravity field, thanks to the availability of freely of charge high resolution global models and to the improvements in the gravity inversion methods, can represent a valid and cheap tool to complete and enhance geophysical modelling of the Earth’s crust. Three tests were carried out: In the first one a simple two-layer problem was considered, while in tests two and three we considered two more realistic scenarios in which the availability on the study area of constraints derived from 3D or 2D seismic surveys were simulated. In all the considered test cases, in which we try to simulate real-life scenarios, the gravity field, inverted by means of an advanced Bayesian technique, was able to obtain a final solution closer to the (simulated) real model than the assumed a priori information, typically halving the uncertainties in the geometries of the main geological horizons with respect to the initial model.
The Central-Eastern Mediterranean region is known to be a complex area due to the interaction of four tectonic plates namely, Arabia, Africa, Anatolia and Eurasia and by the presence of an ancient oceanic crust in the Herodotus and Ionian Basin. The analysis of the available literature highlights that the distribution of the freely available geophysical data (i.e. seismic, gravity and magnetic observations) is quite disparate. In this framework, high resolution global gravity field models, such as XGM2019e, based offshore mainly on satellite data, can be profitably used as a uniform dataset to study in a coherent way large regions. In the current work we exploit XGM2019e model, together with a set of a-priori information, derived mainly from geophysical data retrieved in literature, to study the structure of the crust in the Central-Eastern Mediterranean area. The study is organized in three different phases: in the first one, we enhanced the map of geological crustal provinces of the area by means of an automatic Bayesian classification algorithm applied to second radial derivatives of the gravitational potential. In the second phase, using as observation a grid of gravity anomalies, we applied a full 3D inversion procedure (always based on a Bayesian paradigm) to estimate the mass density variations and the geometries of the main geological units in the whole study area. Finally, in the third phase, we performed a refined 3D inversion on the Cyprus area to improve the modelling of the shallowest layers. The main results of this study, carried out in the framework of the European Space Agency GIADA project, are freely available, upon request, at https://www.g-red.eu/geophysics/. This article is protected by copyright. All rights reserved
In the last decades, the great availability of data and computing power drove the development of powerful machine learning techniques in many research areas, including the ones, as the meteorology, where traditional conceptual models were usually adopted. In this work, we analyze the performance obtained by different techniques in the forecasting of intense rainfall events. A linear classifier, the logistic regression, is used as a benchmark in order to fairly evaluate more complex nonlinear tools: a support vector machine, a deep neural network, and a random forest. Our analysis focuses on both the accuracy and computing effort necessary to identify these models. The nonlinear predictors are proved to outperform the linear baseline model. Under a computational perspective , both neural network and random forest turn out to be more efficient than the support vector machine. The study area we considered is composed of the catchments of four rivers (Lambro, Seveso, Groane, and Olona) in the Lom-bardy region, Northern Italy, just upstream from the highly-urbanized metropolitan area of Milan. Data of intense convective rainfall events from 2010 up to 2017 (more than 600 events) have been used to identify and test the four considered predictors.
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