Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

Big Data Era in Sky and Earth Observation (BIG-SKY-EARTH, http://www.bigskyearth.eu) is COST Action that aims at setting the ground for a long-term networking between astronomy and remote sensing research communities in the area of Big Data utilization. The purpose of BIG-SKY-EARTH is to emphasize similarities between these disciplines and boost the communication within and between the emerging field of astroinformatics and its older Earth Observation counterpart geoinformatics, in close collaboration with computer scientists. The Action is now entering its final year and the results are visible on several scales. There are many examples of concrete "industrial cross-pollination" stories where BIG-SKY-EARTH facilitated exchange of methods and knowledge between network participants. For example, remote sensing and astronomy big data repositories for meteorological nowcasting, thermosolar energy production forecasting, astronomy big data analytics libraries for wind farm predictive maintenance visualization, astronomy and remote sensing C-based stack for scalable numerical analysis used in advanced manufacturing analytics, GPU analytics for remote sensing and industrial analytics, or developing astronomy platform on the top of commercial remote sensing airship to enable transfer the same technology to a high-resolution remote sensing platform. Some of those collaborations expanded into research papers or even project proposals for H2020 based on partnerships between academia and industry, including developing new types of astronomy and remote sensing research based on innovative airship technologies. The Action has also organized three training schools so far: "Big Data Processing" (Oberpfaffenhofen, Germany), "Big Data Visualization" (Preston, UK), "Big Data GPU Analytics" (San Sebastián, Spain). On the level of the entire networking, the Action is also working on the book "Big Data in AstroGeoInformatics" and accompanying code and algorithm repository. Altogether, the established level of activity and interests for further collaboration suggest that this networking will actively continue also after the official end of COST funding. This presentation will also show two examples of research activities that the presenter started thanks to BIG-SKY-EARTH. The first example focuses on the Precipitable Water Vapor (PWV) estimated from Sentinel-1 images using the SAR interferometry technique. Large databases of high resolution Sentinel-1 PWV maps will need to be analyzed before their assimilation in Numerical Weather Models and use for the estimation of geophysical parameters. This research started during an STSM visit at the Finnish Geospatial Research Institute led to the first tools for the analysis of PWV time series in terms of terrain topography and landcover and the visualization of atmosphere thermodynamic quantities [1]. The second example is on the mapping of the Snow Water Equivalent (SWE) using Sentinel-1 SAR images[2-4]. References: [1]G.Nico,A.Gil,M.Quartulli,P.Mateus,J.Catalao,Merging InSAR and GNSS meteorology:how can we mine InSAR and GNSS databases to extract and visualize information on atmosphere processes?,Proc.of Big Data from Space(BIDS),375-378,2017 [2]V.Conde,G.Nico,P.Mateus,J.Catalao,A.Kontu,M.Gritsevich,Snow Water Equivalent Retrieval Using Synthetic Aperture Radar(SAR) Interferometry,Proc. 8th EARSeL workshop on Land Ice and Snow,2017 [3]V.Conde,G.Nico,J.Catalao,A.Kontu,M.Gritsevich,Wide-area mapping of snow water equivalent by Sentinel-1&2 data,Geophysical Research Abstracts Vol.19, EGU2017-9580-1,2017 [4]V.Conde,G.Nico,P.Mateus,J.Catalão,A.Kontu,M.Gritsevich,On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry: a new application for the Sentinel-1 mission,Journal of Hydrology and Hydromechanics,DOI:10.2478/johh-2018-0003,2017

We study the impact of assimilating very high-resolution Precipitable Water Vapor (PWV) maps into a non-hydrostatic Numerical Weather Prediction (NWP) model by the three-dimensional variational (3D-var) technique. PWV maps are obtained by processing the Sentinel-1 Synthetic Aperture Radar (SAR), using the SAR interferometry (InSAR) technique. Changes in the 3D distribution of water vapor, temperature and wind are studied to explain the onset of a deep convection phenomenon. Sentinel-1 images are used to build a time series of PWV maps having a spatial resolution up to 25 m and a time sampling of 6 days. We show that a sub-daily time sampling can be attained if data from different SAR platforms and/or orbits are used, or when future geosynchronous SAR satellites will become operational. The Weather Research Forecasting Data Assimilation (WRFDA) model is used to implement the 3D-Var technique. The finer 3-km domain is centered over the area of interest. A two-way nesting procedure was used. The initial and boundary conditions are set using ECMWF forecasting over Europe are available at very high resolution (0.1°). The InSAR PWV map are assimilated only on the fine domain (3-km). A model spin-up for 6h. For the assimilation the model is initiated at the time of SAR acquisitions and run for 12 hours. The background error covariance matrix B was computed by the National Meteorological Centre (NMC) method, for the finer-resolution domain, where the model perturbations were given by the differences between forecasts (e.g., T + 24 minus T + 12) valid at the same time over a period of one month. We discuss the improvement of the InSAR PWV assimilation in terms of model thermodynamics. Changes in the Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) and Severe Weather Threat Index (SWEAT) are evaluated and used to improve the detection of deep convection onset. A thorough statistical analysis is performed comparing the WRF output with the results obtained by assimilating InSAR and GNSS-based PWV measurements. We show that the assimilation of InSAR data provides an improvement in terms of precipitation and forecast skill score. We analyze also the changes in the 3D distribution of hydrometeors that in the case of storms can significantly contribute to the measured PWV. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015 is presented. The advantage and limitations of assimilating InSAR data into the mesoscale model are discussed. Reference: P. Mateus, J. Catalão, and G. Nico, "Sentinel-1 Interferometric SAR Mapping of Precipitable Water Vapor Over a Country-Spanning Area", IEEE Transactions on Geoscience and Remote Sensing, 55(5), 2993-2999, 2017.

The aim of this paper is to describe how ground-based radar interferometry can provide displacement measurements of earth dam surfaces and of vibration frequencies of its main concrete infrastructures. In many cases, dams were built many decades ago and, at that time, were not equipped with in situ sensors embedded in the structure when they were built. Earth dams have scattering properties similar to landslides for which the Ground-Based Synthetic Aperture Radar (GBSAR) technique has been so far extensively applied to study ground displacements. In this work, SAR and Real Aperture Radar (RAR) configurations are used for the measurement of earth dam surface displacements and vibration frequencies of concrete structures, respectively. A methodology for the acquisition of SAR data and the rendering of results is described. The geometrical correction factor, needed to transform the Line-of-Sight (LoS) displacement measurements of GBSAR into an estimate of the horizontal displacement vector of the dam surface, is derived. Furthermore, a methodology for the acquisition of RAR data and the representation of displacement temporal profiles and vibration frequency spectra of dam concrete structures is presented. For this study a Ku-band ground-based radar, equipped with horn antennas having different radiation patterns, has been used. Four case studies, using different radar acquisition strategies specifically developed for the monitoring of earth dams, are examined. The results of this work show the information that a Ku-band ground-based radar can provide to structural engineers for a non-destructive seismic assessment of earth dams.

Tangible cultural heritage, historical buildings and bridges have an important cultural significance and economic value within the tourism industry and the identity of local communities. The preservation and the assessment of their structural health are important issues which call for multidisciplinary teams and non-invasive monitoring techniques due the uniqueness and historical values of these man-made structures. Numerical models used to study the structural behavior of these historical buildings and bridges under different adverse conditions (eg intense traffic flow, natural hazard events, chemical pollution or simply aging) can benefit from accurate measurements of mechanical properties such as displacements and vibration frequencies, both bringing information about the static and dynamical behavior of such historical constructions. This work presents some results of structural monitoring of man-made structures by Ground-based Synthetic Aperture Radar (GBSAR) interferometry techniques. A ku-band GBSAR interferometer is used to derive displacement maps of the monitored target, with a sub-millimeter precisions. Furthermore, GBSAR interferometry is used to measure vibration frequencies of vertical and horizontal structures, such bell towers, towers, bridges and historical walls. The main advantage of this technique is its capability to operate in any weather and sun-illumination condition, in a truly Non-Destructive Monitoring (NDM) approach, ie without installing any reflector on the observed target.

In this paper we analyze the influence of the geographical position on the increase of the total electron content in the ionospheric D-region during solar X-ray flares. We modeled the total electron content using data related to signals whose propagation paths lie in the mid and both mid and low latitude ionosphere. The obtained results indicate a larger increase of the total electron content in the perturbed equatorial D-region where the solar radiation is more pronounced and causes a larger electron density gradient with altitude.

This paper is focused on visualization of the information extracted by GBSAR data acquired in landslide areas. It describes the way Graphical Processing Units (GPUs) can be used to generate and visualize accurate GBSAR images and displacement maps in near real time. Examples of GBSAR images, as radar coordinates and rendered on Digital Surface Models (DSMs), coherence and displacement maps are shown.

In this study, an experiment aimed to integrate Global Navigation Satellite System (GNSS) atmospheric data with meteorological data into a neural network system is performed. Precipitable Water Vapor (PWV) estimates derived from GNSS are combined with surface pressure, surface temperature and relative humidity obtained continuously from ground-based meteorological stations. The work aims to develop a methodology to forecast short-term intense rainfall. Hence, all the data is sampled at one hour interval. A continuous time series of 3 years of GNSS data from one station in Lisbon, Portugal, is processed. Meteorological data from a nearby meteorological station are collected. Remote sensing data of cloud top from SEVIRI is used, providing collocated data also on an hourly basis. A 3 year time series of hourly accumulated precipitation data are also available for evaluation of the neural network results. In previous studies, it was found that time varying PWV is correlated with rainfall, with a strong increase of PWV peaking just before intense rainfall, and with a strong decrease afterwards. However, a significant amount of false positives was found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work a multilayer fitting network is used to process the GNSS and meteorological data inputs in order to estimate the target outputs, given by the hourly precipitation. It is found that the combination of GNSS data and meteorological variables processed by neural network improves the detection of heavy rainfall events and reduces the number of false positives.

This paper presents a methodology to generate maps of atmosphere's precipitable water vapor (PWV) over large areas with a length of hundreds of kilometers and a width of about 250 km, based on the use of interferometric Sentinel-1A/BC-band synthetic aperture radar (SAR) data with a high spatial resolution of 5 x 20 m(2) and the revisiting time of six days. An algorithm to calibrate and merge PWV maps from different swaths of Sentinel-1 acquired along the same track, using global navigation satellite system (GNSS) measurements, is described. The proposed methodology is tested on Sentinel-1A SAR images acquired over the Iberian Peninsula, along both descending and ascending tracks. The assessment with an independent set of GNSS measurements shows a mean difference of a fraction of millimeter and a dispersion lower than 2 mm. Both the use of Sentinel-1A/B SAR images and the proposed methodology open new perspectives on the application of SAR meteorology for the high-resolution mapping of PWV over large region-spanning areas and the assimilation of interferometric SAR data into numerical weather models.

Hydrogel composite membranes (HCMs) are used as novel mineralization platforms for the bioinspired synthesis of CaCO3 superstructures. A comprehensive statistical analysis of experimental results revealed quantitative relationships between crystallization conditions and crystal texture and the strong selectivity toward complex morphologies when monomers bearing carboxyl and hydroxyl groups are used together in the hydrogel synthesis in HCMs.

The integration of interferometric synthetic aperture radar (InSAR) and GPS tomography techniques for the estimation of the 3-D distribution of atmosphere refractivity is discussed. A methodology to use the maps of the temporal changes of precipitable water vapor (PWV) provided by InSAR as a further constraint in the GPS tomography is described. The aim of the methodology is to increase the accuracy of the GPS tomography reconstruction of the atmosphere's refractivity. The results, which are obtained with SAR and GPS data acquired over the Lisbon area, Portugal, are presented and assessed. It has been found that the reconstruction of the atmospheric refractivity is closer to the real atmospheric state with a mitigation of the smoothing effects due to the usual geometrical constraints of the GPS tomography.

The availability of accurate rainfall data with high spatial resolution, especially in vast watersheds with low density of ground-measurements, is critical for planning and management of water resources and can increase the quality of the hydrological modeling predictions. In this study, we used two classical methods: the optimal interpolation and the successive correction method (SCM), for merging ground-measurements and satellite rainfall estimates. Cressman and Barnes schemes have been used in the SCM in order to define the error covariance matrices. The correction of bias in satellite rainfall data has been assessed by using four different algorithms: (1) the mean bias correction, (2) the regression equation, (3) the distribution transformation, and (4) the spatial transformation. The satellite rainfall data were provided by the Tropical Rainfall Measuring Mission, over the Brazilian Amazon Rainforest. Performances of the two merging data techniques are compared, qualitatively, by visual inspection and quantitatively, by a statistical analysis, collected from January 1999 to December 2010. The computation of the statistical indices shows that the SCM, with the Cressman scheme, provides slightly better results.

This paper studies the problem of the assimilation of precipitable water vapor (PWV), estimated by synthetic aperture radar interferometry, using the Weather Research and Forecast Data Assimilation model 3-D variational data assimilation system. The experiment is designed to assess the impact of the PWV assimilation on the hydrometers and the rainfall predictions during 12 h after the assimilation time. A methodology to obtain calibrated maps of PWV and estimated their precision is also presented. The forecasts are compared with GPS estimates of PWV and with rainfall observations from a meteorological radar. Results show that after data assimilation, there is a correction of the bias in the PWV prediction and an improvement in the prediction of the weak to moderate rainfall up to 9 h after the assimilation time.

A new methodology for the mapping of intertidal terrain morphology is presented. It is based on the use of synthetic aperture radar (SAR) images and the temporal correlation between the SAR backscatter intensity and the water level on the intertidal zone. The proposed methodology does not require manual editing, providing a set of geolocated pixels that can be used to generate a digital elevation model of the intertidal zone. The methodology is validated using TerraSAR-X SAR images acquired over Tagus estuary. This methodology can be useful for the regular updating of intertidal bathymetric models useful for both flood hazard mitigation and morphodynamics modeling.

Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density.

The work deals the monitoring of a single ancient landslide detected in the Vonace area, southwards of Maierato (Calabria, Italy). A 18-hour-measurement campaign has been carried out using the Ground-based Synthetic Aperture Radar (GBSAR) interferometry technique carried between March, 25th and 26th. Displacement maps have been geolocated and overlaid to a Digital Elevation Model of the scene. It has been observed that the Vonace area is almost stable except two portions located at the foot of the ancient landslide and at the centre of the town, respectively. In both cases, a maximum displacement of about 0.5 mm has been measured. A further campaign is needed to confirm this displacement.

In the Grande da Pipa river basin, north of Lisbon, 64 % of the total number of landslides inventoried is totally or partially included in a lithological unit composed by marl, clay, and sandstone intercalation complex that is present in 58 % of the study area. The Persistent Scatterer synthetic aperture radar interferometry technique is applied to a data set of TerraSAR-X SAR images, from April of 2010 to March of 2011, firstly to the Laje-Salema test site and further exported to the Grande da Pipa river basin. This work's specific objectives are the following: (i) to assess the potential of the Persistent Scatterer displacement maps to the identification of new landslides/unstable areas and in the redefinition of landslide limits, (ii) to update the landslide state of activity, and (iii) to evaluate the capacity of the Persistent Scatterer deformation maps in assessing landslide susceptibility at the regional scale. Based on this approach, it was possible to increment the number of landslides and to redefine the landslide limits in the test site in 3.8 %. For 39 landslides, it was possible to update the landslide state of activity, in particular from dormant to reactivated or dormant-reactivated (23 landslides) or from stabilized to reactivated (5 landslides). Landslide susceptibility map based in Persistent Scatterer deformation rates, independently validated with a deep rotational slide map, obtained the best value of area under the curve (0.668).

In this work, we exploit the integration of an advanced synthetic aperture radar (SAR) interferometry technique and the application of the finite-element method for the assessment and the interpretation of a localized subsidence phenomenon that took place within a specific area of Lisbon, Portugal. SAR images over the Lisbon city, covering different time intervals in the period of 1995-2010, were acquired and processed by means of the persistent scatterers (PSs) technique. Results clearly reveals a localized subsidence, limited to an area 2 km × 1.5 km wide, which has been confirmed by the leveling performed in 1976, 1996, and 2010. A physical interpretation of the observed ground deformations is provided based on the results of a finite-element model using stratigraphic data, in situ piezometric measurements, and geotechnical properties of the involved soils. The ground subsidence is interpreted as the consequence of a consolidation process affecting the central fine-grained soil layer, which in turn has been driven by water withdrawal from the existing aquifers. The change of the hydraulic boundary conditions was generated by the excavation works for the construction of underground lines and also by the reduction of rainfall water infiltration as an effect of the increase in ground surface impermeable areas due to urbanization. The consequent consolidation process of the compressible fine-grained soil layer is supposed to provide a reasonable explanation of the observed time series of ground displacement in the examined area.

In this work, we exploit the integration of an advanced synthetic aperture radar (SAR) interferometry technique and the application of the finite-element method for the assessment and the interpretation of a localized subsidence phenomenon that took place within a specific area of Lisbon, Portugal. SAR images over the Lisbon city, covering different time intervals in the period of 1995-2010, were acquired and processed by means of the persistent scatterers (PSs) technique. Results clearly reveals a localized subsidence, limited to an area 2 km × 1.5 km wide, which has been confirmed by the leveling performed in 1976, 1996, and 2010. A physical interpretation of the observed ground deformations is provided based on the results of a finite-element model using stratigraphic data, \textit{in situ} piezometric measurements, and geotechnical properties of the involved soils. The ground subsidence is interpreted as the consequence of a consolidation process affecting the central fine-grained soil layer, which in turn has been driven by water withdrawal from the existing aquifers. The change of the hydraulic boundary conditions was generated by the excavation works for the construction of underground lines and also by the reduction of rainfall water infiltration as an effect of the increase in ground surface impermeable areas due to urbanization. The consequent consolidation process of the compressible fine-grained soil layer is supposed to provide a reasonable explanation of the observed time series of ground displacement in the examined area.