@InProceedings{AstafyevaBNSHKOR:2023:ToAuNe,
               author = "Astafyeva, Elvira and Brissaud, Quentin and Naletckii, Boris and 
                         S{\'a}nchez Juarez, Sa{\'u}l Alejandro and Honda, Rog{\'e}rio 
                         Hisashi and Kherani, Esfhan Alam and Ouar, Ines Dahlia and 
                         Ravanelli, Michela",
          affiliation = "{CNRS - Centre national de la recherche scientifique} and NORSAR 
                         and {Universit{\'e} Paris Cit{\'e}} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Universit{\'e} Paris Cit{\'e}} and {Universit{\'e} 
                         Paris Cit{\'e}}",
                title = "Toward automatic near-real-time detection of travelling 
                         ionospheric disturbances (TIDs) driven by lower atmosphere and 
                         near-surface geophysical events",
            booktitle = "Proceedings...",
                 year = "2023",
         organization = "AGU FAll Meeting",
            publisher = "AGU",
             abstract = "Lower atmosphere and geophysical near-surface events such as 
                         severe weather and natural hazard events generate acoustic and 
                         gravity waves and perturb the ionosphere, generating travelling 
                         ionospheric disturbances (TIDs). The TIDs manifest themselves as 
                         fluctuations of plasma density that propagate as waves. 
                         Near-real-time (NRT) detection, characterization and tracking of 
                         TIDs are of the greatest importance for Space Weather 
                         applications, but also for future monitoring and assessment of 
                         natural hazards from the ionosphere. This contribution will 
                         present our recent developments in the field of automatic NRT 
                         detection of TIDs of different origins in data series of total 
                         electron content (TEC) by GNSS. Recently, two NRT-compatible 
                         methods have been developed by our research team. The first one 
                         can both capture disturbances with high TEC derivative (dTEC/dt) 
                         and determine their velocity and direction of propagation in NRT 
                         (Maletckii \& Astafyeva, SciRep, 2021, doi: 
                         10.1038/s41598-021-99906-5). This method, however, fails to detect 
                         TIDs with lower rate of TEC change. The second technique is based 
                         on Machine Learning to automatically detect disturbances in TEC 
                         data series and to determine the arrival time (Brissaud \& 
                         Astafyeva, GJI, 2022, doi: 10.1093/gji/ggac167). In this 
                         contribution, we will give an overview of the recently developed 
                         monitoring tools for co-seismic travelling ionospheric 
                         disturbances and other TIDs (driven by volcanic eruptions, 
                         tsunamis, tornadoes, large convective storms), and recent progress 
                         regarding both ionospheric dataset curation and new deep learning 
                         technologies. This work is supported by the French National 
                         Research Agency (ANR, grant ANR-22-CE49-0011) and by the French 
                         Space Agency (CNES, project RealDetect).",
  conference-location = "San Francisco, CA",
      conference-year = "11-15 Dec. 2023",
             language = "en",
           targetfile = "Toward automatic near-real-time.pdf",
        urlaccessdate = "06 maio 2024"
}