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  • 1. Baumann, Carsten
    et al.
    McCloskey, Aoife E.
    Timing of the solar wind propagation delay between L1 and Earth based on machine learning2021In: Journal of Space Weather and Space Climate, E-ISSN 2115-7251, Vol. 11, no 41Article in journal (Refereed)
    Abstract [en]

    Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents are severe threats originating from space weather. Knowing the potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study, we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the advanced composition explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined based on of 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min concerning the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50% and 15% respectively. To increase the confidence in the predictions of the trained GB model, we perform a performance validation, provide drop-column feature importance and analyze the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

  • 2. Schillings, Audrey
    et al.
    Palin, Laurianne
    Bower, Gemma E.
    Opgenoorth, Hermann J.
    Milan, Steve E.
    Kauristie, Kirsti
    Juusola, Liisa
    Reeves, Geoff D.
    Henderson, Mike G.
    Paxton, Larry J.
    Lester, Mark
    Hamrin, Maria
    Van de Kamp, Max
    Signatures of wedgelets over Fennoscandia during the St Patrick’s Day Storm 20152023In: Journal of Space Weather and Space Climate, E-ISSN 2115-7251, Vol. 13Article in journal (Refereed)
    Abstract [en]

    During the long main phase of the St Patrick’s Day storm on March 17, 2015, we found three separate enhancements of the westward electrojet. These enhancements are observed in the ionospheric equivalent currents computed using geomagnetic data over Fennoscandia. Using data from the IMAGE magnetometer network, we identified localised field-aligned current (FAC) systems superimposed on the pre-existing ionospheric current system. We suggest that these localised current systems are wedgelets and that they can potentially contribute to a larger-scale structure of a substorm current wedge (SCW). Each wedgelet is associated with a negative BX spike. Each spike is recorded at a higher latitude than the former one and all three are very localised over Fennoscandia. The first spike occurred at 17:34 UT and was observed at Lycksele, Rørvik and Nurmijärvi, the second spike was recorded at 17:41 UT and located at Lycksele and Rørvik, whereas the last spike occurred at 17:47 UT and was observed at Kevo and Abisko. Simultaneous optical auroral data and electron injections at the geosynchronous orbit indicate that one or more substorms took place in the polar ionosphere at the time of the wedgelets. This study demonstrates the occurrence of small and short-lived structures such as wedgelets at different locations over a short time scale, 15 min in this case.

  • 3. Stauning, Peter
    Using PC indices to predict violent GIC events threatening power grids2020In: Journal of Space Weather and Space Climate, E-ISSN 2115-7251, Vol. 10Article in journal (Refereed)
    Abstract [en]

    The aim of the present contribution is to investigate the timing relations between enhancements in the Polar Cap (PC) indices and power grid disturbances related to geomagnetically induced currents (GIC). The polar cap indices, PCN (North) and PCS (South), are based on measurements of geomagnetic variations in the central polar caps. These variations are strongly related to the transpolar convection of plasma and magnetic fields driven by the solar wind. During cases of enhanced merging processes at the front of the magnetosphere and subsequent tailward convection of plasma and embedded magnetic fields, the magnetospheric tail configuration may accumulate excess energy, which upon release may cause violent substorm activity. Earlier reports have disclosed remarkably lengthy intervals, ranging up to several hours, of elevated PC index values preceding GIC-related power grid disruptions. The present investigation has shown that the delays of typically 3–4 h between increases in the PC indices and GIC-related power grid disturbances are related to displacements of the substorm processes responsible for strong GIC events to subauroral latitudes where vulnerable power grids reside. The results have shown that PC index values remaining above an “alert level” of 10 mV/m through more than 1 h indicate a high risk for violent GIC events that may threaten power grids and other vulnerable technical systems. These results support the application of real-time PC indices in space weather monitoring and forecast services.

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