The Ebro Observatory-URL obtains funding to carry out a research project focused on the detection, monitoring and modeling of ionospheric disturbances (MIRA project). It will last for three years and will be led by the researchers Estefania Blanch and David Altadill. In two years, the Ebro Observatory will have obtained funding for three projects through the calls of the AEI (State Agency for Investigation).
This project aims to broaden the knowledge of the effects of solar activity on the ionosphere and the terrestrial magnetic field. On the one hand, the project will allow designing alert systems, which will allow mitigating or alerting about space meteorological events and their effects. On the other hand, the project will broaden the scientific knowledge about the ionospheric irregularities that are produced due to solar activity.
The research group involved in this project has a long history and experience in the detection and monitoring of ionospheric irregularities as evidenced by scientific publications in first-level international journals and the international projects in which they have participated previously, achieved in competitive calls of the European Commission, the European Space Agency and NATO.
Space meteorology studies solar activity and its effects on the Earth and its environment to predict potential impacts on the planet, especially in biological and technological systems. The ionospheric irregularities are variations in ionospheric plasma density that can significantly affect the technological systems based on radioelectric signals.
This financing was obtained from the State Program for the Generation of Knowledge and Scientific and Technological Strengthening of the R & D System of the Ministry of Science, Innovation and Universities of 2018. In the 2017 call, the Ebro Observatory also obtained funding for two projects, a HUMID, studying drought in the Iberian Peninsula using satellite models and data, and the other, IBERGIC, studying the impacts of space meteorology on electric transport networks using modeling and observation tools.