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  • EARTH SCIENCE 〉 SUN-EARTH INTERACTIONS 〉 IONOSPHERE/MAGNETOSPHERE DYNAMICS 〉 PLASMA WAVES  (1)
  • empirical prediction  (1)
  • 1
    Publication Date: 2022-10-14
    Description: Abstract
    Description: This dataset is the MLT-averaged plasmapause position calculated for the NSF GEM Challenge Events. We use the recently developed Plasma density in the Inner magnetosphere Neural network-based Empirical (PINE) model [Zhelavskaya et al., 2017]. The PINE density model was developed using neural networks and was trained on the electron density data set from the Van Allen Probes Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) [Kletzing et al., 2013]. The model reconstructs the plasmasphere dynamics well (with a cross-correlation of ~0.95 on the test set), and its global reconstructions of plasma density are in good agreement with the IMAGE EUV images of the global distribution of He+. We compare the electron number density value given by the PINE model with the density threshold separating plasmaspheric-like and trough-like density given by [Sheeley et al., 2001] and get the plasmapause position in each MLT. Then, we calculate the MLT-averaged plasmapause position. The. time resolution is 1 hour. These data files presenting the Magnetic Local Time (MLT)-averaged plasmapause position used in the simulations in Wang et al [2020]. The data are presented as the following three tabular ASCII files (.dat) : Lpp_PINE_Sheely_Mean_Mar15_Mar20.dat: content, column1 time [day], column 2 L [Re (Earth Radii)] Lpp_PINE_Sheely_Mean_May30_Jun02.dat: content, column1 time [day], column 2 L [Re (Earth Radii)] Lpp_PINE_Sheely_Mean_Sep17_Sep26.dat: content, column1 time [day], column 2 L [Re (Earth Radii)]
    Keywords: Plasmasphere ; Plasmapause ; EARTH SCIENCE 〉 SUN-EARTH INTERACTIONS 〉 IONOSPHERE/MAGNETOSPHERE DYNAMICS 〉 PLASMA WAVES ; EARTH SCIENCE SERVICES 〉 MODELS 〉 SOLAR-ATMOSPHERE/SPACE-WEATHER MODELS
    Type: Dataset , Dataset
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  • 2
    Publication Date: 2021-10-11
    Description: Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.
    Keywords: 538.7 ; Kp index ; geomagnetic activity ; empirical prediction ; solar wind ; forecast ; AI
    Language: English
    Type: map
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