A deep learning approach to solar radio flux forecasting

Stevenson, Emma and Rodriguez Fernandez, Victor and Minisci, Edmondo and Camacho Fernandez, David (2022). A deep learning approach to solar radio flux forecasting. "Acta Astronautica", v. 193 ; pp. 595-606. ISSN 0094-5765. https://doi.org/10.1016/j.actaastro.2021.08.004.


Title: A deep learning approach to solar radio flux forecasting
  • Stevenson, Emma
  • Rodriguez Fernandez, Victor
  • Minisci, Edmondo
  • Camacho Fernandez, David
Item Type: Article
Título de Revista/Publicación: Acta Astronautica
Date: April 2022
ISSN: 0094-5765
Volume: 193
Freetext Keywords: Solar radio flux, Space weather, Deep Learning, Time series forecasting, Ensemble
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Sistemas Informáticos
UPM's Research Group: Applied Intelligence and Data Analysis (AIDA)
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img] PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB)


The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable.

Funding Projects

Horizon 2020813644Stardust-RMassimiliano VasileStardust Reloaded

More information

Item ID: 70042
DC Identifier: https://oa.upm.es/70042/
OAI Identifier: oai:oa.upm.es:70042
DOI: 10.1016/j.actaastro.2021.08.004
Official URL: https://www.sciencedirect.com/science/article/pii/S009457652100415X
Deposited by: Emma Stevenson
Deposited on: 10 Mar 2022 06:21
Last Modified: 10 Apr 2022 22:30
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM