Search Results (16)

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Estimating design hydrographs in small and ungauged basins remains a significant challenge, primarily due to limited hydrometeorological data and the operational complexity of advanced modelling tools. This study presents an interactive digital twin platform to support hydrological modelling in such contexts. The aim of the proposed platform is to integrate three hydrological models—EBA4SUB (event-based rainfall–runoff model), COSMO4SUB (continuous rainfall–runoff model), and Virtual Rain (stochastic rainfall generator)—and automates key pre-processing tasks, including watershed delineation, Curve Number estimation, and rainfall input generation. Built on a three-tier architecture, the system comprises an interactive front end, a back-end database with spatial and meteorological data, and a suite of computational routines developed in Python and C#. The platform was deployed across the Lazio Region (Italy) for basins with contributing areas smaller than 400 km². Users can interactively select watersheds via a map-based interface, obtain preliminary hydrological characterizations, and export model-ready inputs and outputs. The proposed platform offers several advantages: it reduces model preparation time, facilitates access to advanced modelling tools, standardizes input data at the regional level, and ensures reproducible pre-processing workflows. By lowering the technical and time barriers of hydrological modelling, the digital twin provides an effective framework for bringing science-based tools closer to real-world practice. Full article

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output. Full article

In the last few decades there has been increasing interest in the commercial usage of the stratosphere, especially for Earth observation systems. Stratospheric platforms allow Earth monitoring at a regional scale with persistency toward a limited area. For this reason, accurate meteorological forecasts are needed in order to guarantee stationarity. The main aim of this work is to provide a review of wind prediction techniques in the stratosphere, achieved by the most popular global models, such as ECMWF IFS, NCEP GFS and ICON. Then, the capabilities of the COSMO limited area model to reproduce the wind speed in the stratosphere are evaluated considering a model configuration with very high resolution (about 1 km) over a domain located in Southern Italy, assuming the radio sounding data at Pratica di Mare airport as the reference. Vertical profiles were analyzed for selected days, highlighting good performances, though improvements can be achieved by adopting a fifth-order interpolation of the model data. Finally, monthly wind speed time series for selected heights were post-processed by means of fast Fourier transform, revealing the existence of main frequencies and the presence of a scaling regime and a power law of the form f^−β over a broad range of time scales, in the Fourier space. The exponent spectral β is close to the exact 5/3 Kolmogorov value for all the datasets. Full article

The lack of meteorological observations at high latitudes and the small size and relatively short lifetime of polar lows (PLs) constitute a problem in the simulation and prediction of these phenomena by numerical models. On the other hand, PLs, which are rapidly developing, can lead to such extreme weather events as stormy waves, strong winds, the icing of ships, and snowfalls with low visibility, which can influence communication along the Arctic seas. This article is devoted to studying the possibility of the numerical simulation and prediction of polar lows by different model configurations and resolutions. The results of the numerical experiments for the Norwegian and Barents seas with grid spacings of 6.5 and 2 km using the ICON-Ru configurations of the ICON (ICOsahedral Nonhydrostatic) model and with a grid spacing of 6.5 km using the COSMO-CLM (Climate Limited-area Modeling) configuration of the COSMO (COnsortium for Small-scale MOdelling) model are presented for the cold season of 2019–2020. All the used model configurations demonstrated the possibility of the realistic simulation of polar lows. The ICON model showed slightly more accurate results for the analyzed cases. The best results showed runs with lead times of less than a day. Full article

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (β_ext, ϖ, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications. Full article

This study aims to assess the capabilities of a weather forecasting system based on simulations performed with the COSMO (COnsortium for Small-scale Modeling) model over a domain located in southern Italy, employing a spatial resolution of about 1 km, driven by ECMWF-IFS global data. The model is run daily at the Italian Aerospace Research Center (CIRA), and the evaluation was performed from January to May 2018 using a combination of observational data, specifically data provided by the CIRA meteorological station, wind profiler and ceilometer. Moreover, data provided by radio sounding located at Pratica di Mare and ground stations at two other locations were also used. A model configuration optimized through a tuning procedure over the domain considered was employed, while the evaluation was performed by comparing daily values of several variables and using standard monitoring indices. The results highlight that the model has good capability in reproducing daily values of temperature, while precipitation intensity is generally underestimated, even if rain patterns are well captured (alternating rainy and dry days). Good agreement is also reported for wind speed, especially at 100 and 500 m altitude. Regarding radio sounding data, the COSMO model configuration selected can reproduce the vertical profile of temperature and dew point, with the exception of inversion points. Evaluation against ceilometer data is achieved in terms of cloud height and planetary boundary layer height. Full article

Urbanization and hydrology have an interactive relationship, as urbanization changing the hydrology of a system and the hydrology commonly causing structural damage to the infrastructure. Hydrological modelling has been used to quantify the water causing structural impacts, and to provide solutions to the issues. However, in already-urbanized areas, creating a soil map to use as input in the modelling process is difficult, as observation positions are limited and visuals of the natural vegetation which indicate soil distribution are unnatural. This project used historical satellite images in combination with terrain parameters and digital soil mapping methods to produce an accurate (Kappa statistic = 0.81) hydropedology soil map for the Cosmo City suburb in Johannesburg, South Africa. The map was used as input into the HYDRUS 2D and SWAT hydrological models to quantify the water creating road damage at Kampala Crescent, a road within Cosmo City (using HYDRUS 2D), as well as the impact of urbanization on the hydrology of the area (using SWAT). HYDRUS 2D modelling showed that a subsurface drain installed at Kampala Crescent would need a carrying capacity of 0.3 m³·h⁻¹·m⁻¹ to alleviate the road damage, while SWAT modelling shows that surface runoff in Cosmo City will commence with as little rainfall as 2 mm·month⁻¹. This project showcases the value of multidisciplinary work. The remote sensing was invaluable to the mapping, which informed the hydrological modelling and subsequently provided answers to the engineers, who could then mitigate the hydrology-related issues within Cosmo City. Full article

This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations. Full article

A twentieth century-long coupled atmosphere-ocean regional climate simulation with COSMO-CLM (Consortium for Small-Scale Modeling, Climate Limited-area Model) and NEMO (Nucleus for European Modelling of the Ocean) is studied here to evaluate the added value of coupled marginal seas over continental regions. The interactive coupling of the marginal seas, namely the Mediterranean, the North and the Baltic Seas, to the atmosphere in the European region gives a comprehensive modelling system. It is expected to be able to describe the climatological features of this geographically complex area even more precisely than an atmosphere-only climate model. The investigated variables are precipitation and 2 m temperature. Sensitivity studies are used to assess the impact of SST (sea surface temperature) changes over land areas. The different SST values affect the continental precipitation more than the 2 m temperature. The simulated variables are compared to the CRU (Climatic Research Unit) observational data, and also to the HOAPS/GPCC (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, Global Precipitation Climatology Centre) data. In the coupled simulation, added skill is found primarily during winter over the eastern part of Europe. Our analysis shows that, over this region, the coupled system is dryer than the uncoupled system, both in terms of precipitation and soil moisture, which means a decrease in the bias of the system. Thus, the coupling improves the simulation of precipitation over the eastern part of Europe, due to cooler SST values and in consequence, drier soil. Full article

We compare tropospheric delays from Global Navigation Satellite Systems (GNSS) and Synthetic Aperture Radar (SAR) Interferometry (InSAR) in a challenging mountainous environment in the Swiss Alps, where strong spatial variations of the local tropospheric conditions are often observed. Tropospheric delays are usually considered to be an error for both GNSS and InSAR, and are typically removed. However, recently these delays are also recognized as a signal of interest, for example for assimilation into numerical weather models or climate studies. The GNSS and InSAR are techniques of complementary nature, as one has sparse spatial but high temporal resolution, and the other very dense spatial coverage but repeat pass of only a few days. This raises expectations for a combination of these techniques. For this purpose, a comprehensive comparison between the techniques must be first performed. Due to the relative nature of InSAR estimates, we compare the difference slant tropospheric delays ( $dSTD$ ) retrieved from GNSS with the $dSTD$ s estimated using Persistent Scatterer Interferometry (PSI) of 32 COSMO-SkyMed SAR images taken in a snow-free period from June to October between 2008 and 2013. The GNSS estimates calculated at permanent geodetic stations are interpolated to the locations of persistent scatterers using an in-house developed least-squares collocation software COMEDIE. The Pearson’s correlation coefficient between InSAR and GNSS estimates averaged over all acquisitions is equal to 0.64 and larger than 0.8 for approximately half of the layers. Better agreement is obtained mainly for days with high variability of the troposphere (relative to the tropospheric conditions at the time of the reference acquisition), expressed as standard deviations of the GNSS-based $dSTD$ s. On the other hand, the most common feature for the days with poor agreement is represented by very stable, almost constant GNSS estimates. In addition, there is a weak correlation between the agreement and the water vapor values in the area, as well as with the number of stations in the closest vicinity of the study area. Adding low-cost L-1 only GPS stations located within the area of the study increases the biases for most of the dates, but the standard deviations between InSAR and GNSS decrease for the limited area with low-cost stations. Full article

With a wide spectrum of imaging capabilities—from optical to radar sensors, low to very high resolution, continental to local scale, single-image to multi-temporal approaches, yearly to sub-daily acquisition repeat cycles—Earth Observation (EO) offers several opportunities for the geoscience community to map and monitor natural and human-induced Earth hazards from space. The Special Issue “Observing Geohazards from Space” of Geosciences gathers 12 research articles on the development, validation, and implementation of satellite EO data, processing methods, and applications for mapping and monitoring of geohazards such as slow moving landslides, ground subsidence and uplift, and active and abandoned mining-induced ground movements. Papers published in this Special Issue provide novel case studies demonstrating how EO and remote sensing data can be used to detect and delineate land instability and geological hazards in different environmental contexts and using a range of spatial resolutions and image processing methods. Remote sensing datasets used in the Special Issue papers encompass satellite imagery from the ERS-1/2, ENVISAT, RADARSAT-1/2, and Sentinel-1 C-band, TerraSAR-X and COSMO-SkyMed X-band, and ALOS L-band SAR missions; Landsat 7, SPOT-5, WorldView-2/3, and Sentinel-2 multi-spectral data; UAV-derived RGB and near infrared aerial photographs; LiDAR surveying; and GNSS positioning data. Techniques that are showcased include, but are not limited to, differential Interferometric SAR (InSAR) and its advanced approaches such as Persistent Scatterers (PS) and Small Baseline Subset (SBAS) methods to estimate ground deformation, Object-Based Image Analysis (OBIA) to identify landslides in high resolution multi-spectral data, UAV and airborne photogrammetry, Structure-from-Motion (SfM) for digital elevation model generation, aerial photo-interpretation, feature extraction, and time series analysis. Case studies presented in the papers focus on landslides, natural and human-induced subsidence, and groundwater management and mining-related ground deformation in many local to regional-scale study areas in Austria, Belgium, Italy, Slovakia, Spain, and the UK. Full article

Precipitation and evapotranspiration, and in particular the precipitation minus evapotranspiration deficit ( $P-E$ ), are climate variables that may be better represented in reanalyses based on numerical weather prediction (NWP) models than in other datasets. $P-E$ provides essential information on the interaction of the atmosphere with the land surface, which is of fundamental importance for understanding climate change in response to anthropogenic impacts. However, the skill of models in closing the atmospheric-terrestrial water budget is limited. Here, total water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) mission are used in combination with discharge data for assessing the closure of the water budget in the recent high-resolution Consortium for Small-Scale Modelling 6-km Reanalysis (COSMO-REA6) while comparing to global reanalyses (Interim ECMWF Reanalysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)) and observation-based datasets (Global Precipitation Climatology Centre (GPCC), Global Land Evaporation Amsterdam Model (GLEAM)). All 26 major European river basins are included in this study and aggregated to 17 catchments. Discharge data are obtained from the Global Runoff Data Centre (GRDC), and insufficiently long time series are extended by calibrating the monthly Génie Rural rainfall-runoff model (GR2M) against the existing discharge observations, subsequently generating consistent model discharge time series for the GRACE period. We find that for most catchments, COSMO-REA6 closes the water budget within the error estimates. In contrast, the global reanalyses underestimate $P-E$ with up to 20 mm/month. For all models and catchments, short-term (below the seasonal timescale) variability of atmospheric terrestrial flux agrees well with GRACE and discharge data with correlations of about 0.6. Our large study area allows identifying regional patterns like negative trends of $P-E$ in eastern Europe and positive trends in northwestern Europe. Full article

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia. Full article

The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m²/m²). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°). Full article