The overall objective of our project is therefore to design, build and run an innovative end-to-end service for continuous and automatic GNSS deformation monitoring based on a new generation of low-cost GNSS receivers (Global Navigation Satellite System, including GPS and Galileo), prototypes of which are currently being developed by GReD and selected partners. The objective of the feasibility study is the development of a business plan to confirm the potential market and benefits, identify application fields and to estimate the investments needed for designing, building and running an operative end-to-end monitoring service. The business plan will also consider intellectual property management, sales channels to reach the market and economic metrics for the service pricing and delivering. The overall activities of the project are foreseen to be completed within 6 months.

The main objective of the GIMS project is to build and commercialize an advanced low-cost system based on EGNSS, Copernicus SAR and other in-situ sensors, like inertial measurement units, for the purpose of monitoring ground deformations with a focus on landslides and subsidence. The system will recover deformations with millimetric level accuracies and daily acquisition rate. Moreover the integration of in-situ accelerometers will give real-time alerts in case of sudden movements. Finally the low-cost infrastructure deployed for the landslide monitoring can be used as collector of environmental data for smart grids purposes. The observations of these three different monitoring techniques, namely EGNSS, SAR and accelerometers, are complementary in time and space and can be integrated to obtain a better understanding of the monitored processes and a more complete knowledge of the deformation phenomenon. The project involves researchers and industrial developers from different fields: radiofrequency analysis and related hardware design, telecommunications, SAR and GNSS data analysis, accelerometers signal processing, geostatistics, geology. Significant effort will be put in the cooperation between different areas and in the interface between different products and technologies. The overall purpose of the GIMS project is to have detailed and timely knowledge of the geophysical behaviour of parts of the Earth surface, and its hindrances on structures, in order to mitigate casualties and injuries to the population, and better plan maintenance intervention.

Geo-energies, such as geothermal energy, CO2 storage and underground energy storage, have a great potential to contribute to meet the Paris Agreement targets on climate change. Yet, their deployment has been hindered by a lack of a full understanding of the processes that are induced in the subsurface by large-scale fluid injection/extraction. The various processes involved (e.g., fluid flow, geomechanical, geochemical and thermal effects) imply complex interactions that cannot be predicted without considering the dominant coupled processes, which is rarely done. As a result, some early geo-energy projects have occasionally developed unpredicted consequences, such as felt and damaging induced earthquakes, gas leakage and aquifer contamination, dampening public perception on geo-energies. SMILE aims at overcoming these challenges in developing geo-energy solutions by training a new generation of young researchers that will become experts in understanding and predicting coupled processes. Thus, they will be capable of proposing innovative solutions for the successful deployment of subsurface low-carbon energy sources while protecting groundwater and related ecosystems. To achieve this ambitious goal, the early-stage researchers will be exposed to an interdisciplinary training on experimental, mathematical and numerical modelling of coupled processes, upscaling techniques and ground deformation monitoring using field data from highly instrumented pilot tests and industrial sites. The training in SMILE has been designed by both academic and industrial partners to train competitive researchers with both technical-scientific and transferable skills to enhance their employability in academia, industry and public sector. The outputs of the project will be largely disseminated. Outreach to society will be achieved through a conspicuous series of initiatives. SMILE will make a significant contribution to the societal challenges of securing clean and low-carbon energy sources.

The SINOPTICA project aims at exploiting the untapped potential of assimilating remote sensing (EO-derived and ground-based radar) as well GNSS-derived datasets and in situ weather stations data into very high-resolution, very short-range numerical weather forecasts to provide improved prediction of extreme weather events to the benefit of ATM operations. This will be done by setting up a continuously updated database of remote sensing-derived, GNSS-derived and in situ weather stations variables, in combination with an automated assimilation system to feed an NWM. The usefulness of deploying dedicated networks of sensors to monitor atmospheric variables at high spatial resolution in the vicinity of ATM "hotspots" such as airports will be investigated as well. SINOPTICA weather forecast results will be integrated into ATM decision-support tools, visualizing weather information on the controller's display, and generating new 4D trajectories to avoid severe weather areas. The usefulness of the newly developed SINOPTICA tools will be monitored during the project and evaluated, thanks to the involvement of ATM stakeholders in the project consortium and advisory board.

The MAGDA project aims at developing a toolchain for atmosphere monitoring, weather forecasting, and severe weather/irrigation/crop monitoring advisory, with GNSS (including Galileo) at its core, to provide useful information to agricultural operators. MAGDA will exploit the untapped potential of assimilating GNSS-derived, drone-derived, Copernicus EO-derived datasets, in situ weather sensors into very high-resolution, short-range (1-2 days ahead) and very short-range (less than 1 day ahead) numerical weather forecasts to provide improved prediction of severe weather events (rainfall, snow, hail, wind, heat and cold waves) as well as of weather-driven agriculture pests and diseases to the benefit of agriculture operations, also in light of ongoing effects of climate change. These targets will be achieved by setting up a database of variables of interest, and an assimilation system to feed a numerical weather prediction model, which in turn drives a hydrological model for irrigation performance and water accounting to assess water use and related productivity. In addition to already existing observational networks, new dedicated networks of sensors, including GNSS and drones, to monitor atmospheric variables at high spatial resolution will be deployed in the vicinity of large farms and cultivated areas, to provide data with high spatial and temporal resolutions for the assimilation into the weather model. The delivery of the augmented forecasts and irrigation advisories to farmers will be enabled by a dedicated dashboard and APIs to already existing Farm Management Systems. The tools developed within MAGDA will represent the technical and methodological components based on which services to support agricultural operations will be defined.