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Data mining (DM) has ushered in a new era of empirical magnetic reconstructions of the magnetosphere via application of the k-nearest neighbors (kNN) method. In this approach, the combined magnetosphere storm-substorm state is characterized by the  Sym - H  and  AL  indices, their time derivatives, and the solar wind electric field    v    B    z    I  M  F. However, using the DM reconstructions to account for the substorm contributions to the ring current as well as describing storm-time substorms remains a problem. The inner region  r  ≤ 12 R   E  , where the ring current develops, has a much higher density of data than the tail region 12 R   E   ≤  r  ≤ 22 R   E  , where substorms operate. This results in two models inconsistent in their scales dictated by the corresponding data densities. The inner model reconstructs storm time dynamics, including the formation of the westward and eastward ring current and pressure distributions. The outer model captures substorm features, including the thinning and rapid dipolarization of the tail sheet during the growth and expansion phases, respectively. However, the substorm model is insufficient to reconstruct the eastward ring current while the storm model cannot fully reproduce substorm effects because it overfits in the tail region. This issue is addressed by constructing a hybrid model which is fit using virtual magnetic field observations generated by sampling the other two models. The resulting merged resolution model concurrently captures the spatial scales associated with both storms in the inner region and substorms in the near-tail region. Hence it is particularly useful for investigation of the storm-substorm relationship, including storm-time substorms and the impact of individual substorm injections to the buildup of the storm-time ring current.

Concurrent Empirical Magnetic Reconstruction of Storm and Substorm Spatial Scales Using Data Mining and Virtual Spacecraft