The coupling between the magnetosphere and solar wind contributes to the energy, momentum, and mass transfer between the systems. However, geomagnetic pulsations facilitate the continuation of this process in the magnetosphere and the production of discrete auroral arcs. Therefore, remote-sensing the magnetospheric conditions. Data analytics with machine learning (ML) gives insight into scalability, adaptability, and feature extraction compared to traditional empirical models. The availability of big data in the Svalbard network spanning 25years from 1996 motivated the current study. Hence, we present the forecasting of auroral Pc5 pulsations from solar wind parameters using the ML technique. In the training phase, there was a regression of 0.75 and MSE=11.90 nT2. The relationship between Pc5 forecast and observations in low and high geomagnetic activity and solar activity showed good consistency with R=0.76 and MSE= 11.4 nT2. For instance, the model adapted well to the St. Patrick geomagnetic storm of March 17th, 2015 despite uncertainties in the data. In addition, the model also adapted well with stunning performance in all Svalbard observatories with HOP leading with 6949 prediction events and NAL with the least. Thus, this was consistent with previous studies in terms of Pc5 pulsations latitudinal or L-shell dependence. Finally, validation with Kp and F10.7 indices presented excellent coherence between the models. Overall, The ML studied the connection between solar wind and interplanetary magnetic field properties to the ground magnetic field perturbations with good correlation results. Hence, the model will be fit for use by the magnetospheric community for space weather studies.

The coupling between the magnetosphere and solar wind contributes to the energy, momentum, and mass transfer between the systems. However, geomagnetic pulsations facilitate the continuation of this process in the magnetosphere and the production of discrete auroral arcs. Therefore, remote-sensing the magnetospheric conditions. Data analytics with machine learning (ML) gives insight into scalability, adaptability, and feature extraction compared to traditional empirical models. The availability of big data in the Svalbard network spanning 25years from 1996 motivated the current study. Hence, we present the forecasting of auroral Pc5 pulsations from solar wind parameters using the ML technique. In the training phase, there was a regression of 0.75 and MSE=11.90 nT2. The relationship between Pc5 forecast and observations in low and high geomagnetic activity and solar activity showed good consistency with R=0.76 and MSE= 11.4 nT2. For instance, the model adapted well to the St. Patrick geomagnetic storm of March 17th, 2015 despite uncertainties in the data. In addition, the model also adapted well with stunning performance in all Svalbard observatories with HOP leading with 6949 prediction events and NAL with the least. Thus, this was consistent with previous studies in terms of Pc5 pulsations latitudinal or L-shell dependence. Finally, validation with Kp and F10.7 indices presented excellent coherence between the models. Overall, The ML studied the connection between solar wind and interplanetary magnetic field properties to the ground magnetic field perturbations with good correlation results. Hence, the model will be fit for use by the magnetospheric community for space weather studies.

Ground Pi-2 pulsations comprise superpositions of various modal components of shear and fast Alfven waves, field line resonance, and plasmaspheric resonances. These complex waveforms, hard to resolve with Fourier transforms are successfully characterized by wavelet techniques. Wavelet detection employs decomposition and reconstruction modes to characterize time-frequency components. Hence, suitable for the examination of the locality and complexity of natural signal patterns. The current study presents the automatic detection of Pi-2 pulsations using Daubechies and Morlet wavelet transforms. In the study, distinct Pi-2 events from CPMN stations along 210${^\circ}$ magnetic meridian were detected. Global Pi-2 pulsations with harmonious H oscillations and discrete D bays in the sub-aurora zone suggest a common source with diverse tunneling paths. Scalograms of Pi-2 undulations of the frequency band of 6.7-22 mHz were observed despite different kinds of Pi-2s. Auroral Pi-2s were highly localized in local time with clear H and D bays, implying magnetospheric-ionospheric current couplings. Latitudinal and longitudinal Pi-2 propagations are exemplified by 180${^\circ}$ phase-shift (polarization) in EWA and group delay in the mid-latitudes of the northern hemisphere. Overall, Pi-2 wave power from high to low latitudes declined with peak amplitudes of 15 nT to less than 1 nT, respectively. Finally, external influences from sea currents causing signal attenuation due to the station's proximity to the sea were also identified. To conclude, the accuracy and efficiency of wavelet analysis with no computation hassle render it a valuable tool for the study of space events in the magnetospheric community.