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Titel:

Machine Learning Approach for Forecasting Space Weather Effects in the Ionosphere with Uncertainty Quantification

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
Natras R., Soja B., Schmidt M., Dominique M., Türkmen A.
Abstract:
Space weather can cause strong sudden disturbances in the Earth’s ionosphere that can degrade the performance and reliability of Global Navigation Satellite System (GNSS) operations. To minimize such degradations, ionospheric effects need to be precisely and timely corrected by providing information of the spatially and temporally variable Total Electron Content (TEC). To obtain such corrections and early warning information of space weather events, we need to model the nonlinear space weather p...     »
Stichworte:
Machine Learning, Space Weather, Ionosphere, Vertical Total Electron Content (VTEC), Forecasting, Uncertainty Quantification
Kongress- / Buchtitel:
European Geosciences Union (EGU) General Assembly
Datum der Konferenz:
2022-05-23 - 2022-05-27
Jahr:
2022
Jahr / Monat:
2022-05
Print-ISBN:
EGU22-5408
Volltext / DOI:
doi:https://doi.org/10.5194/egusphere-egu22-5408
WWW:
https://meetingorganizer.copernicus.org/EGU22/EGU22-5408.html
TUM Einrichtung:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
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