In the case of very intense geomagnetic storms, satellites and power grids can be damaged, as well as extensive pipeline systems such as those used to transport gas, oil and water ( kasinskii2007effect). This increases the intensity of the Earth’s ring current, which is reflected by the Dst index ( akasofu1981energy gonzalez1994geomagnetic). When magnetic reconnection happens between the IMF and the Earth’s magnetosphere, an influx of energetic particles from the solar wind into the magnetosphere occurs. They are caused by the coupling between solar wind and magnetosphere, in particular the southward component of the interplanetary magnetic field (IMF) ( burton1975empirical). Geomagnetic storms are large perturbations in the Earth’s magnetic field. Finally, different methodologies for training the neural network are explored in order to remove the persistence behavior from the results. The new measure, based on Dynamical Time Warping, is capable of identifying results made by the persistence model and shows promising results in confirming the visual observations of the neural network’s output.
#Omniweb dst series#
In this work, a new method is proposed to measure whether two time series are shifted in time with respect to each other, such as the persistence model output versus the observation. However, visual inspection showed that the predictions made by the neural network were behaving similarly to the persistence model. Inspection of the model’s results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications. To show where the classical metrics are lacking, we trained a neural network, using a long short-term memory network, to make a forecast of the disturbance storm time index at origin time t with a forecasting horizon of 1 up to 6 hours, trained on OMNIWeb data. However, these classical metrics sometimes fail to capture crucial behavior. These models are evaluated with metrics such as the root-mean-square error (RMSE) and Pearson correlation coefficient. In particular, the forecasting of geomagnetic indices with neural network models is becoming a popular field of study. Models based on neural networks and machine learning are seeing a rise in popularity in space physics.
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