A neural network-based local model for prediction of geomagnetic disturbances

Research output: Contribution to journalArticle


This study shows how locally observed geomagnetic disturbances can bepredicted from solar wind data with artificial neural network (ANN)techniques. After subtraction of a secularly varying base level, thehorizontal components X<SUB>Sq</SUB> and Y<SUB>Sq</SUB> of the quiettime daily variations are modeled with radial basis function networkstaking into account seasonal and solar activity modulations. Theremaining horizontal disturbance components DeltaX and DeltaY aremodeled with gated time delay networks taking local time and solar winddata as input. The observed geomagnetic field is not used as input tothe networks, which thus constitute explicit nonlinear mappings from thesolar wind to the locally observed geomagnetic disturbances. The ANNsare applied to data from Sodankylä Geomagnetic Observatory locatednear the peak of the auroral zone. It is shown that 73% of the DeltaXvariance, but only 34% of the DeltaY variance, is predicted from asequence of solar wind data. The corresponding results for prediction ofall transient variations X<SUB>Sq</SUB>+DeltaX andY<SUB>Sq</SUB>+DeltaY are 74% and 51%, respectively. The local timemodulations of the prediction accuracies are shown, and the qualitativeagreement between observed and predicted values are discussed. If drivenby real-time data measured upstream in the solar wind, the ANNs heredeveloped can be used for short-term forecasting of the locally observedgeomagnetic activity.


  • Hans Gleisner
  • Henrik Lundstedt
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Astronomy, Astrophysics and Cosmology


  • Ionosphere: Current systems, Ionosphere: Modeling and forecasting, Magnetospheric Physics: Solar wind/magnetosphere interactions, Mathematical Geophysics: Modeling
Original languageEnglish
Pages (from-to)8425-8434
JournalJournal of Geophysical Research
Issue numberA5
Publication statusPublished - 2001
Publication categoryResearch