Abstract. In the past years several operational D st forecasting algorithms, based on both IMF and solar wind plasma parameters, have been developed and used. We describe an Artificial Neural Network (ANN) algorithm which calculates the D st index on the basis of IMF data only and discuss its performance for several individual storms. Moreover, we briefly comment on the physical grounds which allow the D st forecasting based on IMF only.

High-energy, long gamma-ray bursts (GRBs) can be generated by the core collapse of massive stars at the end of their lives. When they happen in the close-by universe they can be exceptionally bright, as seen from the Earth in the case of the recent, giant, long-lasting GRB221009A. GRB221009A was produced by a collapsing star with a redshift of 0.152: this event was observed by many gamma-ray space experiments, which also detected an extraordinary long gamma-ray afterglow. The exceptionally large fluence of the prompt emission of about 0.013 erg cm−2 illuminated a large geographical region centered on India and including Europe and Asia. We report in this paper the observation of sudden electron flux changes correlated with GRB221009A and measured by the HEPP-L charged particle detector on board the China Seismo-Electromagnetic Satellite, which was orbiting over Europe at the time of the GRB event. The time structure of the observed electron flux closely matches the very distinctive time dependence of the photon flux associated with the main part of the emission at around 13:20 UTC on 2022 October 9. To test the origin of these signals, we set up a simplified simulation of one HEPP-L subdetector: the results of this analysis suggest that the signals observed are mostly due to electrons created within the aluminum collimator surrounding the silicon detector, providing real-time monitoring of the very intense photon fluxes. We discuss the implications of this observation for existing and forthcoming particle detectors on low Earth orbits.

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Abstract. To define a background in the electromagnetic emissions above seismic regions, it is necessary to define the statistical distribution of the wave energy in the absence of seismic activity and any other anomalous input (e.g. solar forcing). This paper presents a completely new method to determine both the environmental and instrumental backgrounds applied to the entire DEMETER satellite electric and magnetic field data over L'Aquila. Our technique is based on a new data analysis tool called ALIF (adaptive local iterative filtering, Cicone et al., 2016; Cicone and Zhou, 2017; Piersanti et al., 2017b). To evaluate the instrumental background, we performed a multiscale statistical analysis in which the instantaneous relative energy (ϵrel), kurtosis, and Shannon entropy were calculated. To estimate the environmental background, a map, divided into 1∘×1∘ latitude–longitude cells, of the averaged relative energy (ϵrel‾), has been constructed, taking into account the geomagnetic activity conditions, the presence of seismic activity, and the local time sector of the satellite orbit. Any distinct signal different (over a certain threshold) from both the instrumental and environmental backgrounds will be considered as a case event to be investigated. Interestingly, on 4 April 2009, when DEMETER flew exactly over L'Aquila at UT = 20:29, an anomalous signal was observed at 333 Hz on both the electric and magnetic field data, whose characteristics seem to be related to pre-seismic activity.